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Is It Time for the Virtual Scientific Conference?: Coronavirus, Travel Restrictions, Conferences Cancelled

Curator: Stephen J. Williams, PhD.

UPDATED 3/12/2020

To many of us scientists, presenting and attending scientific meetings, especially international scientific conferences, are a crucial tool for disseminating and learning new trends and cutting edge findings occurring in our respective fields.  Large international meetings, like cancer focused meetings like AACR (held in the spring time), AAAS and ASCO not only highlight the past years great discoveries but are usually the first place where breakthroughs are made known to the scientific/medical community as well as the public.  In addition these conferences allow for scientists to learn some of the newest technologies crucial for their work in vendor exhibitions.

During the coronavirus pandemic, multiple cancellations of business travel, conferences, and even university based study abroad programs are being cancelled and these cancellations are now hitting the 2020 Spring and potentially summer scientific/medical conferences.  Indeed one such conference hosted by Amgen in Massachusetts was determined as an event where some attendees tested positive for the virus, and as such, now other attendees are being asked to self quarantine.

Today I received two emails on conference cancellations, one from Experimental Biology in California and another from The Cancer Letter, highlighting other conferences, including National Cancer Coalition Network (NCCN) meetings which had been canceled.

 

Experimental Biology - San Diego 2020 - April 4-7

Dear Stephen,

After thoughtful deliberations, the leaders of the Experimental Biology host societies have made the difficult but necessary decision to cancel Experimental Biology (EB) 2020 set to take place April 4–7 in San Diego, California. We know how much EB means to everyone, and we did not make this decision in haste. The health and safety of our members, attendees, their students, our staff, partners and our communities are our top priority.

As we have previously communicated via email, on experimentalbiology.org and elsewhere, EB leadership has been closely monitoring the spread of COVID-19 (coronavirus disease). Based on the latest guidance from public health officials, the travel bans implemented by different institutions and the state of emergency declared in California less than 48 hours ago, it became clear to us that canceling was the right course of action.

We thank you and the entire EB community for understanding the extreme difficulty of this decision and for your commitment to the success of this conference – from the thousands of attendees to the presenters, exhibitors and sponsors who shared their time, expertise, collaboration and leadership. We deeply appreciate your contributions to this community.

What Happens Next?

Everyone who has registered to attend the meeting will receive a full registration refund within the next 45 days. Once your registration cancellation is processed, you will receive confirmation in a separate email. You do not need to contact anyone at EB or your host society to initiate the process. Despite the cancellation of the meeting, we are pleased to tell you that we will publish abstracts in the April 2020 issue of The FASEB Journal as originally planned. Please remember to cancel any personal arrangements you’ve made, such as travel and housing reservations. 

We ask for patience as we evaluate our next steps, and we will alert you as additional information becomes available please see our FAQs for details.

And in The Cancer Letter

Coronavirus vs. oncology: Meeting cancellations, travel restrictions, fears about drug supply chain

By Alexandria Carolan

NOTE: An earlier version of this story was published March 4 on the web and was updated March 6 to include information about restricted travel for employees of cancer centers, meeting cancellations, potential disruptions to the drug supply chain, and funds allocated by U.S. Congress for combating the coronavirus.

Further updates will be posted as the story develops.

Forecasts of the inevitable spread of coronavirus can be difficult to ignore, especially at a time when many of us are making travel plans for this spring’s big cancer meetings.

The decision was made all the more difficult earlier this week, as cancer centers and at least one biotechnology company—Amgen—implemented travel bans that are expected to last through the end of March and beyond. The Cancer Letter was able to confirm such travel bans at Fred Hutchinson Cancer Research Center, MD Anderson Cancer Center, and Dana-Farber Cancer Institute.

Meetings are getting cancelled in all fields, including oncology:

The National Comprehensive Cancer Network March 5 postponed its 2020 annual conference of about 1,500 attendees March 19-22 in Orlando, citing precautions against coronavirus.

“The health and safety of our attendees and the patients they take care of is our number one concern,” said Robert W. Carlson, chief executive officer of NCCN. “This was an incredibly difficult and disappointing decision to have to make. However, our conference attendees work to save the lives of immunocompromised people every day. Some of them are cancer survivors themselves, particularly at our patient advocacy pavilion. It’s our responsibility, in an abundance of caution, to safeguard them from any potential exposure to COVID-19.”

UPDATED 3/12/2020

And today the AACR canceled its yearly 2020 Meeting (https://www.aacr.org/meeting/aacr-annual-meeting-2020/coronavirus-information/)

The American Association for Cancer Research (AACR) Board of Directors has made the difficult decision, after careful consideration and comprehensive evaluation of currently available information related to the novel coronavirus (COVID-19) outbreak, to terminate the AACR Annual Meeting 2020, originally scheduled for April 24-29 in San Diego, California. A rescheduled meeting is being planned for later this year.

The AACR has been closely monitoring the rapidly increasing domestic and worldwide developments during the last several weeks related to COVID-19. This evidence-based decision was made after a thorough review and discussion of all factors impacting the Annual Meeting, including the U.S. government’s enforcement of restrictions on international travelers to enter the U.S.; the imposition of travel restrictions issued by U.S. government agencies, cancer centers, academic institutions, and pharmaceutical and biotech companies; and the counsel of infectious disease experts. It is clear that all of these elements significantly affect the ability of delegates, speakers, presenters of proffered papers, and exhibitors to participate fully in the Annual Meeting.

The health, safety, and security of all Annual Meeting attendees and the patients and communities they serve are the AACR’s highest priorities. While we believe that the decision to postpone the meeting is absolutely the correct one to safeguard our meeting participants from further potential exposure to the coronavirus, we also understand that this is a disappointing one for our stakeholders. There had been a great deal of excitement about the meeting, which was expected to be the largest ever AACR Annual Meeting, with more than 7,400 proffered papers, a projected total of 24,000 delegates from 80 countries and more than 500 exhibitors. We recognize that the presentation of new data, exchange of information, and opportunities for collaboration offered by the AACR Annual Meeting are highly valued by the entire cancer research community, and we are investigating options for rescheduling the Annual Meeting in the near future.

We thank all of our stakeholders for their patience and support at this time. Additional information regarding hotel reservation cancellations, registration refunds, and meeting logistics is available on the FAQ page on the AACR website. We will announce the dates and location of the rescheduled AACR Annual Meeting 2020 as soon as they are confirmed. Our heartfelt sympathies go out to everyone impacted by this global health crisis.

However,  according to both Dr. Fauci and Dr. Scott Gottlieb (former FDA director)  the outbreak may revisit the US and the world in the fall (see https://www.cnbc.com/2020/03/04/were-losing-valuable-time-ex-fda-chief-says-of-coronavirus-spread.html)  therefore these meetings may be cancelled for the whole year.

Is It Time For the Virtual (Real-Time) Conference?

Readers of this Online Access Journal are familiar with our ongoing commitment to open science and believe that forming networks of scientific experts in various fields using a social strategy is pertinent to enhancing the speed, reproducibility and novelty of important future scientific/medical discoveries.  Some of these ideas are highlighted in the following articles found on this site:

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

Twitter is Becoming a Powerful Tool in Science and Medicine

e-Scientific Publishing: The Competitive Advantage of a Powerhouse for Curation of Scientific Findings and Methodology Development for e-Scientific Publishing – LPBI Group, A Case in Point

Reconstructed Science Communication for Open Access Online Scientific Curation

In addition, we understand the importance of communicating the latest scientific/medical discoveries in an open and rapid format, accessible over the social media platforms.  To this effect we have developed a methodology for real time conference coverage

see  Press and Conference Coverage

at  https://pharmaceuticalintelligence.com/press-coverage/

AND

The Process of Real Time Coverage using Social Media

at https://pharmaceuticalintelligence.com/press-coverage/part-one-the-process-of-real-time-coverage-using-social-media/

Using these strategies we are able to communicate, in real time, analysis of conference coverage for a multitude of conferences.

Has technology and social media platforms now have enabled our ability to rapidly communicate, in a more open access platform, seminal discoveries and are scientists today amenable to virtual type of meetings including displaying abstracts using a real-time online platform?

Some of the Twitter analytics we have curated from such meetings show that conference attendees are rapidly adopting such social platforms to communicate with their peers and colleagues meeting notes.

Statistical Analysis of Tweet Feeds from the 14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

Word Associations of Twitter Discussions for 10th Annual Personalized Medicine Conference at the Harvard Medical School, November 12-13, 2014

Comparative Analysis of the Level of Engagement for Four Twitter Accounts: @KDNuggets (Big Data) @GilPress @Forbes @pharma_BI @AVIVA1950

Twitter Analytics on the Inside 3DPrinting Conference #I3DPConf

 

Other Twitter analyses of Conferences Covered by LPBI in Real Time have produced a similar conclusion: That conference attendees are very engaged over social media networks to discuss, share, and gain new insights into material presented at these conferences, especially international conferences.

And although attracting international conferences is lucrative to many cities, the loss in revenue to organizations, as well as the loss of intellectual capital is indeed equally as great.  

Maybe there is room for such type of conferences in the future, and attending by a vast more audience than currently capable. And perhaps the #openscience movement like @MozillaScience can collaborate with hackathons to produce the platforms for such an online movement of scientific conferences as a Plan B.

Other articles on Real Time Conference Coverage in the Online Open Access Journal Include:

Innovations in electronic Scientific Publishing (eSP): Case Studies in Marketing eContent, Curation Methodology, Categories of Research Functions, Interdisciplinary conceptual innovations by Cross Section of Categories, Exposure to Frontiers of Science by Real Time Press coverage of Scientific Conferences

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets by @pharma_BI and by @AVIVA1950: Real Time Coverage and eProceedings of The 11th Annual Personalized Medicine Conference, November 18-19, 2015, Harvard Medical School

REAL TIME Cancer Conference Coverage: A Novel Methodology for Authentic Reporting on Presentations and Discussions launched via Twitter.com @ The 2nd ANNUAL Sachs Cancer Bio Partnering & Investment Forum in Drug Development, 19th March 2014 • New York Academy of Sciences • USA

Search Results for ‘Real Time Conference’

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Live Conference Coverage @Medcitynews Converge 2018 @Philadelphia: Promising Drugs and Breaking Down Silos

Reporter: Stephen J. Williams, PhD

Promising Drugs, Pricing and Access

The drug pricing debate rages on. What are the solutions to continuing to foster research and innovation, while ensuring access and affordability for patients? Can biosimilars and generics be able to expand market access in the U.S.?

Moderator: Bunny Ellerin, Director, Healthcare and Pharmaceutical Management Program, Columbia Business School
Speakers:
Patrick Davish, AVP, Global & US Pricing/Market Access, Merck
Robert Dubois M.D., Chief Science Officer and Executive Vice President, National Pharmaceutical Council
Gary Kurzman, M.D., Senior Vice President and Managing Director, Healthcare, Safeguard Scientifics
Steven Lucio, Associate Vice President, Pharmacy Services, Vizient

What is working and what needs to change in pricing models?

Robert:  He sees so many players in the onStevencology space discovering new drugs and other drugs are going generic (that is what is working).  However are we spending too much on cancer care relative to other diseases (their initiative Going Beyond the Surface)

Steven:  the advent of biosimilars is good for the industry

Patrick:  large effort in oncology, maybe too much (750 trials on Keytruda) and he says pharma is spending on R&D (however clinical trials take large chunk of this money)

Robert: cancer has gotten a free ride but cost per year relative to benefit looks different than other diseases.  Are we overinvesting in cancer or is that a societal decision

Gary:  maybe as we become more specific with precision medicines high prices may be a result of our success in specifically targeting a mutation.  We need to understand the targeted drugs and outcomes.

Patrick: “Cancer is the last big frontier” but he says prices will come down in most cases.  He gives the example of Hep C treatment… the previous only therapeutic option was a very toxic yearlong treatment but the newer drugs may be more cost effective and safer

Steven: Our blockbuster drugs could diffuse the expense but now with precision we can’t diffuse the expense over a large number of patients

President’s Cancer Panel Recommendation

Six recommendations

  1. promoting value based pricing
  2. enabling communications of cost
  3. financial toxicity
  4. stimulate competition biosimilars
  5. value based care
  6. invest in biomedical research

Patrick: the government pricing regime is hurting.  Alot of practical barriers but Merck has over 200 studies on cost basis

Robert:  many concerns/impetus started in Europe on pricing as they are a set price model (EU won’t pay more than x for a drug). US is moving more to outcomes pricing. For every one health outcome study three studies did not show a benefit.  With cancer it is tricky to establish specific health outcomes.  Also Medicare gets best price status so needs to be a safe harbor for payers and biggest constraint is regulatory issues.

Steven: They all want value based pricing but we don’t have that yet and there is a challenge to understand the nuances of new therapies.  Hard to align all the stakeholders together so until some legislation starts to change the reimbursement-clinic-patient-pharma obstacles.  Possibly the big data efforts discussed here may help align each stakeholders goals.

Gary: What is the data necessary to understand what is happening to patients and until we have that information it still will be complicated to determine where investors in health care stand at in this discussion

Robert: on an ICER methods advisory board: 1) great concern of costs how do we determine fair value of drug 2) ICER is only game in town, other orgs only give recommendations 3) ICER evaluates long term value (cost per quality year of life), budget impact (will people go bankrupt)

4) ICER getting traction in the public eye and advocates 5) the problem is ICER not ready for prime time as evidence keeps changing or are they keeping the societal factors in mind and they don’t have total transparancy in their methodology

Steven: We need more transparency into all the costs associated with the drug and therapy and value-based outcome.  Right now price is more of a black box.

Moderator: pointed to a recent study which showed that outpatient costs are going down while hospital based care cost is going rapidly up (cost of site of care) so we need to figure out how to get people into lower cost setting

Breaking Down Silos in Research

“Silo” is healthcare’s four-letter word. How are researchers, life science companies and others sharing information that can benefit patients more quickly? Hear from experts at institutions that are striving to tear down the walls that prevent data from flowing.

Moderator: Vini Jolly, Executive Director, Woodside Capital Partners
Speakers:
Ardy Arianpour, CEO & Co-Founder, Seqster @seqster
Lauren Becnel, Ph.D., Real World Data Lead for Oncology, Pfizer
Rakesh Mathew, Innovation, Research, & Development Lead, HealthShareExchange
David Nace M.D., Chief Medical Officer, Innovaccer

Seqster: Seqster is a secure platform that helps you and your family manage medical records, DNA, fitness, and nutrition data—all in one place. Founder has a genomic sequencing background but realized sequence  information needs to be linked with medical records.

HealthShareExchange.org :

HealthShare Exchange envisions a trusted community of healthcare stakeholders collaborating to deliver better care to consumers in the greater Philadelphia region. HealthShare Exchange will provide secure access to health information to enable preventive and cost-effective care; improve quality of patient care; and facilitate care transitions. They have partnered with multiple players in healthcare field and have data on over 7 million patients.

Innovacer

Data can be overwhelming, but it doesn’t have to be this way. To drive healthcare efficiency, we designed a modular suite of products for a smooth transition into a data-driven world within 4 weeks. Why does it take so much money to move data around and so slowly?

What is interoperatibility?

Ardy: We knew in genomics field how to build algorithms to analyze big data but how do we expand this from a consumer standpoint and see and share your data.

Lauren: how can we use the data between patients, doctors, researchers?  On the research side genomics represent only 2% of data.  Silos are one issue but figuring out the standards for data (collection, curation, analysis) is not set. Still need to improve semantic interoperability. For example Flatiron had good annotated data on male metastatic breast cancer.

David: Technical interopatabliltiy (platform), semantic interopatability (meaning or word usage), format (syntactic) interopatibility (data structure).  There is technical interoperatiblity between health system but some semantic but formats are all different (pharmacies use different systems and write different prescriptions using different suppliers).  In any value based contract this problem is a big issue now (we are going to pay you based on the quality of your performance then there is big need to coordinate across platforms).  We can solve it by bringing data in real time in one place and use mapping to integrate the format (need quality control) then need to make the data democratized among players.

Rakesh:  Patients data should follow the patient. Of Philadelphia’s 12 health systems we had a challenge to make data interoperatable among them so tdhey said to providers don’t use portals and made sure hospitals were sending standardized data. Health care data is complex.

David: 80% of clinical data is noise. For example most eMedical Records are text. Another problem is defining a patient identifier which US does not believe in.

 

 

 

 

Please follow on Twitter using the following #hash tags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

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How Will FDA’s new precisionFDA Science 2.0 Collaboration Platform Protect Data?

Reporter: Stephen J. Williams, Ph.D.

As reported in MassDevice.com

FDA launches precisionFDA to harness the power of scientific collaboration

FDA VoiceBy: Taha A. Kass-Hout, M.D., M.S. and Elaine Johanson

Imagine a world where doctors have at their fingertips the information that allows them to individualize a diagnosis, treatment or even a cure for a person based on their genes. That’s what President Obama envisioned when he announced his Precision Medicine Initiative earlier this year. Today, with the launch of FDA’s precisionFDA web platform, we’re a step closer to achieving that vision.

PrecisionFDA is an online, cloud-based, portal that will allow scientists from industry, academia, government and other partners to come together to foster innovation and develop the science behind a method of “reading” DNA known as next-generation sequencing (or NGS). Next Generation Sequencing allows scientists to compile a vast amount of data on a person’s exact order or sequence of DNA. Recognizing that each person’s DNA is slightly different, scientists can look for meaningful differences in DNA that can be used to suggest a person’s risk of disease, possible response to treatment and assess their current state of health. Ultimately, what we learn about these differences could be used to design a treatment tailored to a specific individual.

The precisionFDA platform is a part of this larger effort and through its use we want to help scientists work toward the most accurate and meaningful discoveries. precisionFDA users will have access to a number of important tools to help them do this. These tools include reference genomes, such as “Genome in the Bottle,” a reference sample of DNA for validating human genome sequences developed by the National Institute of Standards and Technology. Users will also be able to compare their results to previously validated reference results as well as share their results with other users, track changes and obtain feedback.

Over the coming months we will engage users in improving the usability, openness and transparency of precisionFDA. One way we’ll achieve that is by placing the code for the precisionFDA portal on the world’s largest open source software repository, GitHub, so the community can further enhance precisionFDA’s features.Through such collaboration we hope to improve the quality and accuracy of genomic tests – work that will ultimately benefit patients.

precisionFDA leverages our experience establishing openFDA, an online community that provides easy access to our public datasets. Since its launch in 2014, openFDA has already resulted in many novel ways to use, integrate and analyze FDA safety information. We’re confident that employing such a collaborative approach to DNA data will yield important advances in our understanding of this fast-growing scientific field, information that will ultimately be used to develop new diagnostics, treatments and even cures for patients.

fda-voice-taha-kass-1x1Taha A. Kass-Hout, M.D., M.S., is FDA’s Chief Health Informatics Officer and Director of FDA’s Office of Health Informatics. Elaine Johanson is the precisionFDA Project Manager.

 

The opinions expressed in this blog post are the author’s only and do not necessarily reflect those of MassDevice.com or its employees.

So What Are the Other Successes With Such Open Science 2.0 Collaborative Networks?

In the following post there are highlighted examples of these Open Scientific Networks and, as long as

  • transparancy
  • equal contributions (lack of heirarchy)

exists these networks can flourish and add interesting discourse.  Scientists are already relying on these networks to collaborate and share however resistance by certain members of an “elite” can still exist.  Social media platforms are now democratizing this new science2.0 effort.  In addition the efforts of multiple biocurators (who mainly work for love of science) have organized the plethora of data (both genomic, proteomic, and literature) in order to provide ease of access and analysis.

Science and Curation: The New Practice of Web 2.0

Curation: an Essential Practice to Manage “Open Science”

The web 2.0 gave birth to new practices motivated by the will to have broader and faster cooperation in a more free and transparent environment. We have entered the era of an “open” movement: “open data”, “open software”, etc. In science, expressions like “open access” (to scientific publications and research results) and “open science” are used more and more often.

Curation and Scientific and Technical Culture: Creating Hybrid Networks

Another area, where there are most likely fewer barriers, is scientific and technical culture. This broad term involves different actors such as associations, companies, universities’ communication departments, CCSTI (French centers for scientific, technical and industrial culture), journalists, etc. A number of these actors do not limit their work to popularizing the scientific data; they also consider they have an authentic mission of “culturing” science. The curation practice thus offers a better organization and visibility to the information. The sought-after benefits will be different from one actor to the next.

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

  • Using Curation and Science 2.0 to build Trusted, Expert Networks of Scientists and Clinicians

Given the aforementioned problems of:

        I.            the complex and rapid deluge of scientific information

      II.            the need for a collaborative, open environment to produce transformative innovation

    III.            need for alternative ways to disseminate scientific findings

CURATION MAY OFFER SOLUTIONS

        I.            Curation exists beyond the review: curation decreases time for assessment of current trends adding multiple insights, analyses WITH an underlying METHODOLOGY (discussed below) while NOT acting as mere reiteration, regurgitation

 

      II.            Curation providing insights from WHOLE scientific community on multiple WEB 2.0 platforms

 

    III.            Curation makes use of new computational and Web-based tools to provide interoperability of data, reporting of findings (shown in Examples below)

 

Therefore a discussion is given on methodologies, definitions of best practices, and tools developed to assist the content curation community in this endeavor

which has created a need for more context-driven scientific search and discourse.

However another issue would be Individual Bias if these networks are closed and protocols need to be devised to reduce bias from individual investigators, clinicians.  This is where CONSENSUS built from OPEN ACCESS DISCOURSE would be beneficial as discussed in the following post:

Risk of Bias in Translational Science

As per the article

Risk of bias in translational medicine may take one of three forms:

  1. a systematic error of methodology as it pertains to measurement or sampling (e.g., selection bias),
  2. a systematic defect of design that leads to estimates of experimental and control groups, and of effect sizes that substantially deviate from true values (e.g., information bias), and
  3. a systematic distortion of the analytical process, which results in a misrepresentation of the data with consequential errors of inference (e.g., inferential bias).

This post highlights many important points related to bias but in summarry there can be methodologies and protocols devised to eliminate such bias.  Risk of bias can seriously adulterate the internal and the external validity of a clinical study, and, unless it is identified and systematically evaluated, can seriously hamper the process of comparative effectiveness and efficacy research and analysis for practice. The Cochrane Group and the Agency for Healthcare Research and Quality have independently developed instruments for assessing the meta-construct of risk of bias. The present article begins to discuss this dialectic.

  • Information dissemination to all stakeholders is key to increase their health literacy in order to ensure their full participation
  • threats to internal and external validity  represent specific aspects of systematic errors (i.e., bias)in design, methodology and analysis

So what about the safety and privacy of Data?

A while back I did a post and some interviews on how doctors in developing countries are using social networks to communicate with patients, either over established networks like Facebook or more private in-house networks.  In addition, these doctor-patient relationships in developing countries are remote, using the smartphone to communicate with rural patients who don’t have ready access to their physicians.

Located in the post Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

I discuss some of these problems in the following paragraph and associated posts below:

Mobile Health Applications on Rise in Developing World: Worldwide Opportunity

According to International Telecommunication Union (ITU) statistics, world-wide mobile phone use has expanded tremendously in the past 5 years, reaching almost 6 billion subscriptions. By the end of this year it is estimated that over 95% of the world’s population will have access to mobile phones/devices, including smartphones.

This presents a tremendous and cost-effective opportunity in developing countries, and especially rural areas, for physicians to reach patients using mHealth platforms.

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

In Summary, although there are restrictions here in the US governing what information can be disseminated over social media networks, developing countries appear to have either defined the regulations as they are more dependent on these types of social networks given the difficulties in patient-physician access.

Therefore the question will be Who Will Protect The Data?

For some interesting discourse please see the following post

Atul Butte Talks on Big Data, Open Data and Clinical Trials

 

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Yay! Bloomberg View Seems to Be On the Side of the Lowly Scientist!

 

Reporter: Stephen J. Williams, Ph.D.

Justin Fox at BloombergView had just published an article near and dear to the hearts of all those #openaccess scientists and those of us @Pharma_BI and @MozillaScience who feel strong about #openscience #opendata and the movement to make scientific discourse freely accessible.

His article “Academic Publishing Can’t Remain Such a Great Business” discusses the history of academic publishing and how consolidation of smaller publishers into large scientific publishing houses (Bigger publishers bought smaller ones) has produced a monopoly like environment in which prices for journal subscriptions are rising. He also discusses how the open access movement is challenging this model and may oneday replace the big publishing houses.

A few tidbits from his article:

Publishers of academic journals have a great thing going. They generally don’t pay for the articles they publish, or for the primary editing and peer reviewing essential to preparing them for publication (they do fork over some money for copy editing). Most of this gratis labor is performed by employees of academic institutions. Those institutions, along with government agencies and foundations, also fund all the research that these journal articles are based upon.

Yet the journal publishers are able to get authors to sign over copyright to this content, and sell it in the form of subscriptions to university libraries. Most journals are now delivered in electronic form, which you think would cut the cost, but no, the price has been going up and up:

 

This isn’t just inflation at work: in 1994, journal subscriptions accounted for 51 percent of all library spending on information resources. In 2012 it was 69 percent.

Who exactly is getting that money? The largest academic publisher is Elsevier, which is also the biggest, most profitable division of RELX, the Anglo-Dutch company that was known until February as Reed Elsevier.

 

RELX reports results in British pounds; I converted to dollars in part because the biggest piece of the company’s revenue comes from the U.S. And yes, those are pretty great operating-profit margins: 33 percent in 2014, 39 percent in 2013. The next biggest academic publisher is Springer Nature, which is closely held (by German publisher Holtzbrinck and U.K. private-equity firm BC Partners) but reportedly has annual revenue of about $1.75 billion. Other biggies that are part of publicly traded companies include Wiley-Blackwell, a division of John Wiley & Sons; Wolters Kluwer Health, a division of Wolters Kluwer; and Taylor & Francis, a division of Informa.

And gives a brief history of academic publishing:

The history here is that most early scholarly journals were the work of nonprofit scientific societies. The goal was to disseminate research as widely as possible, not to make money — a key reason why nobody involved got paid. After World War II, the explosion in both the production of and demand for academic research outstripped the capabilities of the scientific societies, and commercial publishers stepped into the breach. At a time when journals had to be printed and shipped all over the world, this made perfect sense.

Once it became possible to effortlessly copy and disseminate digital files, though, the economics changed. For many content producers, digital copying is a threat to their livelihoods. As Peter Suber, the director of Harvard University’s Office for Scholarly Communication, puts it in his wonderful little book, “Open Access”:

And while NIH Tried To Force These Houses To Accept Open Access:

About a decade ago, the universities and funding agencies began fighting back. The National Institutes of Health in the U.S., the world’s biggest funder of medical research, began requiring in 2008 that all recipients of its grants submit electronic versions of their final peer-reviewed manuscripts when they are accepted for publication in journals, to be posted a year later on the NIH’s open-access PubMed depository. Publishers grumbled, but didn’t want to turn down the articles.

Big publishers are making $ by either charging as much as they can or focus on new customers and services

For the big publishers, meanwhile, the choice is between positioning themselves for the open-access future or maximizing current returns. In its most recent annual report, RELX leans toward the latter while nodding toward the former:

Over the past 15 years alternative payment models for the dissemination of research such as “author-pays” or “author’s funder-pays” have emerged. While it is expected that paid subscription will remain the primary distribution model, Elsevier has long invested in alternative business models to address the needs of customers and researchers.

Elsevier’s extra services can add news avenues of revenue

https://www.elsevier.com/social-sciences/business-and-management

https://www.elsevier.com/rd-solutions

but they may be seeing the light on OpenAccess (possibly due to online advocacy, an army of scientific curators and online scientific communities):

Elsevier’s Mendeley and Academia.edu – How We Distribute Scientific Research: A Case in Advocacy for Open Access Journals

SAME SCIENTIFIC IMPACT: Scientific Publishing – Open Journals vs. Subscription-based

e-Recognition via Friction-free Collaboration over the Internet: “Open Access to Curation of Scientific Research”

Indeed we recently put up an interesting authored paper “A Patient’s Perspective: On Open Heart Surgery from Diagnosis and Intervention to Recovery” (free of charge) letting the community of science freely peruse and comment, and generally well accepted by both author and community as a nice way to share academic discourse without the enormous fees, especially on opinion papers in which a rigorous peer review may not be necessary.

But it was very nice to see a major news outlet like Bloomberg View understand the lowly scientist’s aggravations.

Thanks Bloomberg!

 

 

 

 

 

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Artificial Intelligence Versus the Scientist: Who Will Win?

Will DARPA Replace the Human Scientist: Not So Fast, My Friend!

Writer, Curator: Stephen J. Williams, Ph.D.

scientistboxingwithcomputer

Last month’s issue of Science article by Jia You “DARPA Sets Out to Automate Research”[1] gave a glimpse of how science could be conducted in the future: without scientists. The article focused on the U.S. Defense Advanced Research Projects Agency (DARPA) program called ‘Big Mechanism”, a $45 million effort to develop computer algorithms which read scientific journal papers with ultimate goal of extracting enough information to design hypotheses and the next set of experiments,

all without human input.

The head of the project, artificial intelligence expert Paul Cohen, says the overall goal is to help scientists cope with the complexity with massive amounts of information. As Paul Cohen stated for the article:

“‘

Just when we need to understand highly connected systems as systems,

our research methods force us to focus on little parts.

                                                                                                                                                                                                               ”

The Big Mechanisms project aims to design computer algorithms to critically read journal articles, much as scientists will, to determine what and how the information contributes to the knowledge base.

As a proof of concept DARPA is attempting to model Ras-mutation driven cancers using previously published literature in three main steps:

  1. Natural Language Processing: Machines read literature on cancer pathways and convert information to computational semantics and meaning

One team is focused on extracting details on experimental procedures, using the mining of certain phraseology to determine the paper’s worth (for example using phrases like ‘we suggest’ or ‘suggests a role in’ might be considered weak versus ‘we prove’ or ‘provide evidence’ might be identified by the program as worthwhile articles to curate). Another team led by a computational linguistics expert will design systems to map the meanings of sentences.

  1. Integrate each piece of knowledge into a computational model to represent the Ras pathway on oncogenesis.
  2. Produce hypotheses and propose experiments based on knowledge base which can be experimentally verified in the laboratory.

The Human no Longer Needed?: Not So Fast, my Friend!

The problems the DARPA research teams are encountering namely:

  • Need for data verification
  • Text mining and curation strategies
  • Incomplete knowledge base (past, current and future)
  • Molecular biology not necessarily “requires casual inference” as other fields do

Verification

Notice this verification step (step 3) requires physical lab work as does all other ‘omics strategies and other computational biology projects. As with high-throughput microarray screens, a verification is needed usually in the form of conducting qPCR or interesting genes are validated in a phenotypical (expression) system. In addition, there has been an ongoing issue surrounding the validity and reproducibility of some research studies and data.

See Importance of Funding Replication Studies: NIH on Credibility of Basic Biomedical Studies

Therefore as DARPA attempts to recreate the Ras pathway from published literature and suggest new pathways/interactions, it will be necessary to experimentally validate certain points (protein interactions or modification events, signaling events) in order to validate their computer model.

Text-Mining and Curation Strategies

The Big Mechanism Project is starting very small; this reflects some of the challenges in scale of this project. Researchers were only given six paragraph long passages and a rudimentary model of the Ras pathway in cancer and then asked to automate a text mining strategy to extract as much useful information. Unfortunately this strategy could be fraught with issues frequently occurred in the biocuration community namely:

Manual or automated curation of scientific literature?

Biocurators, the scientists who painstakingly sort through the voluminous scientific journal to extract and then organize relevant data into accessible databases, have debated whether manual, automated, or a combination of both curation methods [2] achieves the highest accuracy for extracting the information needed to enter in a database. Abigail Cabunoc, a lead developer for Ontario Institute for Cancer Research’s WormBase (a database of nematode genetics and biology) and Lead Developer at Mozilla Science Lab, noted, on her blog, on the lively debate on biocuration methodology at the Seventh International Biocuration Conference (#ISB2014) that the massive amounts of information will require a Herculaneum effort regardless of the methodology.

Although I will have a future post on the advantages/disadvantages and tools/methodologies of manual vs. automated curation, there is a great article on researchinformation.infoExtracting More Information from Scientific Literature” and also see “The Methodology of Curation for Scientific Research Findings” and “Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison” for manual curation methodologies and A MOD(ern) perspective on literature curation for a nice workflow paper on the International Society for Biocuration site.

The Big Mechanism team decided on a full automated approach to text-mine their limited literature set for relevant information however was able to extract only 40% of relevant information from these six paragraphs to the given model. Although the investigators were happy with this percentage most biocurators, whether using a manual or automated method to extract information, would consider 40% a low success rate. Biocurators, regardless of method, have reported ability to extract 70-90% of relevant information from the whole literature (for example for Comparative Toxicogenomics Database)[3-5].

Incomplete Knowledge Base

In an earlier posting (actually was a press release for our first e-book) I had discussed the problem with the “data deluge” we are experiencing in scientific literature as well as the plethora of ‘omics experimental data which needs to be curated.

Tackling the problem of scientific and medical information overload

pubmedpapersoveryears

Figure. The number of papers listed in PubMed (disregarding reviews) during ten year periods have steadily increased from 1970.

Analyzing and sharing the vast amounts of scientific knowledge has never been so crucial to innovation in the medical field. The publication rate has steadily increased from the 70’s, with a 50% increase in the number of original research articles published from the 1990’s to the previous decade. This massive amount of biomedical and scientific information has presented the unique problem of an information overload, and the critical need for methodology and expertise to organize, curate, and disseminate this diverse information for scientists and clinicians. Dr. Larry Bernstein, President of Triplex Consulting and previously chief of pathology at New York’s Methodist Hospital, concurs that “the academic pressures to publish, and the breakdown of knowledge into “silos”, has contributed to this knowledge explosion and although the literature is now online and edited, much of this information is out of reach to the very brightest clinicians.”

Traditionally, organization of biomedical information has been the realm of the literature review, but most reviews are performed years after discoveries are made and, given the rapid pace of new discoveries, this is appearing to be an outdated model. In addition, most medical searches are dependent on keywords, hence adding more complexity to the investigator in finding the material they require. Third, medical researchers and professionals are recognizing the need to converse with each other, in real-time, on the impact new discoveries may have on their research and clinical practice.

These issues require a people-based strategy, having expertise in a diverse and cross-integrative number of medical topics to provide the in-depth understanding of the current research and challenges in each field as well as providing a more conceptual-based search platform. To address this need, human intermediaries, known as scientific curators, are needed to narrow down the information and provide critical context and analysis of medical and scientific information in an interactive manner powered by web 2.0 with curators referred to as the “researcher 2.0”. This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Yaneer Bar-Yam of the New England Complex Systems Institute was not confident that using details from past knowledge could produce adequate roadmaps for future experimentation and noted for the article, “ “The expectation that the accumulation of details will tell us what we want to know is not well justified.”

In a recent post I had curated findings from four lung cancer omics studies and presented some graphic on bioinformatic analysis of the novel genetic mutations resulting from these studies (see link below)

Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for

Non-Small Cell Lung Cancer

which showed, that while multiple genetic mutations and related pathway ontologies were well documented in the lung cancer literature there existed many significant genetic mutations and pathways identified in the genomic studies but little literature attributed to these lung cancer-relevant mutations.

KEGGinliteroanalysislungcancer

  This ‘literomics’ analysis reveals a large gap between our knowledge base and the data resulting from large translational ‘omic’ studies.

Different Literature Analyses Approach Yeilding

A ‘literomics’ approach focuses on what we don NOT know about genes, proteins, and their associated pathways while a text-mining machine learning algorithm focuses on building a knowledge base to determine the next line of research or what needs to be measured. Using each approach can give us different perspectives on ‘omics data.

Deriving Casual Inference

Ras is one of the best studied and characterized oncogenes and the mechanisms behind Ras-driven oncogenenis is highly understood.   This, according to computational biologist Larry Hunt of Smart Information Flow Technologies makes Ras a great starting point for the Big Mechanism project. As he states,” Molecular biology is a good place to try (developing a machine learning algorithm) because it’s an area in which common sense plays a minor role”.

Even though some may think the project wouldn’t be able to tackle on other mechanisms which involve epigenetic factors UCLA’s expert in causality Judea Pearl, Ph.D. (head of UCLA Cognitive Systems Lab) feels it is possible for machine learning to bridge this gap. As summarized from his lecture at Microsoft:

“The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analyzing their causal roots.”

According to him first

1) articulate assumptions

2) define research question in counter-inference terms

Then it is possible to design an inference system using calculus that tells the investigator what they need to measure.

To watch a video of Dr. Judea Pearl’s April 2013 lecture at Microsoft Research Machine Learning Summit 2013 (“The Mathematics of Causal Inference: with Reflections on Machine Learning”), click here.

The key for the Big Mechansism Project may me be in correcting for the variables among studies, in essence building a models system which may not rely on fully controlled conditions. Dr. Peter Spirtes from Carnegie Mellon University in Pittsburgh, PA is developing a project called the TETRAD project with two goals: 1) to specify and prove under what conditions it is possible to reliably infer causal relationships from background knowledge and statistical data not obtained under fully controlled conditions 2) develop, analyze, implement, test and apply practical, provably correct computer programs for inferring causal structure under conditions where this is possible.

In summary such projects and algorithms will provide investigators the what, and possibly the how should be measured.

So for now it seems we are still needed.

References

  1. You J: Artificial intelligence. DARPA sets out to automate research. Science 2015, 347(6221):465.
  2. Biocuration 2014: Battle of the New Curation Methods [http://blog.abigailcabunoc.com/biocuration-2014-battle-of-the-new-curation-methods]
  3. Davis AP, Johnson RJ, Lennon-Hopkins K, Sciaky D, Rosenstein MC, Wiegers TC, Mattingly CJ: Targeted journal curation as a method to improve data currency at the Comparative Toxicogenomics Database. Database : the journal of biological databases and curation 2012, 2012:bas051.
  4. Wu CH, Arighi CN, Cohen KB, Hirschman L, Krallinger M, Lu Z, Mattingly C, Valencia A, Wiegers TC, John Wilbur W: BioCreative-2012 virtual issue. Database : the journal of biological databases and curation 2012, 2012:bas049.
  5. Wiegers TC, Davis AP, Mattingly CJ: Collaborative biocuration–text-mining development task for document prioritization for curation. Database : the journal of biological databases and curation 2012, 2012:bas037.

Other posts on this site on include: Artificial Intelligence, Curation Methodology, Philosophy of Science

Inevitability of Curation: Scientific Publishing moves to embrace Open Data, Libraries and Researchers are trying to keep up

A Brief Curation of Proteomics, Metabolomics, and Metabolism

The Methodology of Curation for Scientific Research Findings

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

The growing importance of content curation

Data Curation is for Big Data what Data Integration is for Small Data

Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation The Art of Scientific & Medical Curation

Exploring the Impact of Content Curation on Business Goals in 2013

Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison

conceived: NEW Definition for Co-Curation in Medical Research

Reconstructed Science Communication for Open Access Online Scientific Curation

Search Results for ‘artificial intelligence’

 The Simple Pictures Artificial Intelligence Still Can’t Recognize

Data Scientist on a Quest to Turn Computers Into Doctors

Vinod Khosla: “20% doctor included”: speculations & musings of a technology optimist or “Technology will replace 80% of what doctors do”

Where has reason gone?

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Twitter is Becoming a Powerful Tool in Science and Medicine

 Curator: Stephen J. Williams, Ph.D.

Updated 4/2016

Life-cycle of Science 2

A recent Science article (Who are the science stars of Twitter?; Sept. 19, 2014) reported the top 50 scientists followed on Twitter. However, the article tended to focus on the use of Twitter as a means to develop popularity, a sort of “Science Kardashian” as they coined it. So the writers at Science developed a “Kardashian Index (K-Index) to determine scientists following and popularity on Twitter.

Now as much buzz Kim Kardashian or a Perez Hilton get on social media, their purpose is solely for entertainment and publicity purposes, the Science sort of fell flat in that it focused mainly on the use of Twitter as a metric for either promotional or public outreach purposes. A notable scientist was mentioned in the article, using Twitter feed to gauge the receptiveness of his presentation. In addition, relying on Twitter for effective public discourse of science is problematic as:

  • Twitter feeds are rapidly updated and older feeds quickly get buried within the “Twittersphere” = LIMITED EXPOSURE TIMEFRAME
  • Short feeds may not provide the access to appropriate and understandable scientific information (The Science Communication Trap) which is explained in The Art of Communicating Science: traps, tips and tasks for the modern-day scientist. “The challenge of clearly communicating the intended scientific message to the public is not insurmountable but requires an understanding of what works and what does not work.” – from Heidi Roop, G.-Martinez-Mendez and K. Mills

However, as highlighted below, Twitter, and other social media platforms are being used in creative ways to enhance the research, medical, and bio investment collaborative, beyond a simple news-feed.  And the power of Twitter can be attributed to two simple features

  1. Ability to organize – through use of the hashtag (#) and handle (@), Twitter assists in the very important task of organizing, indexing, and ANNOTATING content and conversations. A very great article on Why the Hashtag in Probably the Most Powerful Tool on Twitter by Vanessa Doctor explains how hashtags and # search may be as popular as standard web-based browser search. Thorough annotation is crucial for any curation process, which are usually in the form of database tags or keywords. The use of # and @ allows curators to quickly find, index and relate disparate databases to link annotated information together. The discipline of scientific curation requires annotation to assist in the digital preservation, organization, indexing, and access of data and scientific & medical literature. For a description of scientific curation methodologies please see the following links:

Please read the following articles on CURATION

The Methodology of Curation for Scientific Research Findings

Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison

Science and Curation: The New Practice of Web 2.0

  1. Information Analytics

Multiple analytic software packages have been made available to analyze information surrounding Twitter feeds, including Twitter feeds from #chat channels one can set up to cover a meeting, product launch etc.. Some of these tools include:

Twitter Analytics – measures metrics surrounding Tweets including retweets, impressions, engagement, follow rate, …

Twitter Analytics – Hashtags.org – determine most impactful # for your Tweets For example, meeting coverage of bioinvestment conferences or startup presentations using #startup generates automatic retweeting by Startup tweetbot @StartupTweetSF.

 

  1. Tweet Sentiment Analytics

Examples of Twitter Use

A. Scientific Meeting Coverage

In a paper entitled Twitter Use at a Family Medicine Conference: Analyzing #STFM13 authors Ranit Mishori, MD, Frendan Levy, MD, and Benjamin Donvan analyzed the public tweets from the 2013 Society of Teachers of Family Medicine (STFM) conference bearing the meeting-specific hashtag #STFM13. Thirteen percent of conference attendees (181 users) used the #STFM13 to share their thoughts on the meeting (1,818 total tweets) showing a desire for social media interaction at conferences but suggesting growth potential in this area. As we have also seen, the heaviest volume of conference-tweets originated from a small number of Twitter users however most tweets were related to session content.

However, as the authors note, although it is easy to measure common metrics such as number of tweets and retweets, determining quality of engagement from tweets would be important for gauging the value of Twitter-based social-media coverage of medical conferences.

Thea authors compared their results with similar analytics generated by the HealthCare Hashtag Project, a project and database of medically-related hashtag use, coordinated and maintained by the company Symplur.  Symplur’s database includes medical and scientific conference Twitter coverage but also Twitter usuage related to patient care. In this case the database was used to compare meeting tweets and hashtag use with the 2012 STFM conference.

These are some of the published journal articles that have employed Symplur (www.symplur.com) data in their research of Twitter usage in medical conferences.

B. Twitter Usage for Patient Care and Engagement

Although the desire of patients to use and interact with their physicians over social media is increasing, along with increasing health-related social media platforms and applications, there are certain obstacles to patient-health provider social media interaction, including lack of regulatory framework as well as database and security issues. Some of the successes and issues of social media and healthcare are discussed in the post Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

However there is also a concern if social media truly engages the patient and improves patient education. In a study of Twitter communications by breast cancer patients Tweeting about breast cancer, authors noticed Tweeting was a singular event. The majority of tweets did not promote any specific preventive behavior. The authors concluded “Twitter is being used mostly as a one-way communication tool.” (Using Twitter for breast cancer prevention: an analysis of breast cancer awareness month. Thackeray R1, Burton SH, Giraud-Carrier C, Rollins S, Draper CR. BMC Cancer. 2013;13:508).

In addition a new poll by Harris Interactive and HealthDay shows one third of patients want some mobile interaction with their physicians.

Some papers cited in Symplur’s HealthCare Hashtag Project database on patient use of Twitter include:

C. Twitter Use in Pharmacovigilance to Monitor Adverse Events

Pharmacovigilance is the systematic detection, reporting, collecting, and monitoring of adverse events pre- and post-market of a therapeutic intervention (drug, device, modality e.g.). In a Cutting Edge Information Study, 56% of pharma companies databases are an adverse event channel and more companies are turning to social media to track adverse events (in Pharmacovigilance Teams Turn to Technology for Adverse Event Reporting Needs). In addition there have been many reports (see Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter) that show patients are frequently tweeting about their adverse events.

There have been concerns with using Twitter and social media to monitor for adverse events. For example FDA funded a study where a team of researchers from Harvard Medical School and other academic centers examined more than 60,000 tweets, of which 4,401 were manually categorized as resembling adverse events and compared with the FDA pharmacovigilance databases. Problems associated with such social media strategy were inability to obtain extra, needed information from patients and difficulty in separating the relevant Tweets from irrelevant chatter.  The UK has launched a similar program called WEB-RADR to determine if monitoring #drug_reaction could be useful for monitoring adverse events. Many researchers have found the adverse-event related tweets “noisy” due to varied language but had noticed many people do understand some principles of causation including when adverse event subsides after discontinuing the drug.

However Dr. Clark Freifeld, Ph.D., from Boston University and founder of the startup Epidemico, feels his company has the algorithms that can separate out the true adverse events from the junk. According to their web site, their algorithm has high accuracy when compared to the FDA database. Dr. Freifeld admits that Twitter use for pharmacovigilance purposes is probably a starting point for further follow-up, as each patient needs to fill out the four-page forms required for data entry into the FDA database.

D. Use of Twitter in Big Data Analytics

Published on Aug 28, 2012

http://blogs.ischool.berkeley.edu/i29…

Course: Information 290. Analyzing Big Data with Twitter
School of Information
UC Berkeley

Lecture 1: August 23, 2012

Course description:
How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered.

This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.

Other posts on this site on USE OF SOCIAL MEDIA AND TWITTER IN HEALTHCARE and Conference Coverage include:

Methodology for Conference Coverage using Social Media: 2014 MassBio Annual Meeting 4/3 – 4/4 2014, Royal Sonesta Hotel, Cambridge, MA

Strategy for Event Joint Promotion: 14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

REAL TIME Cancer Conference Coverage: A Novel Methodology for Authentic Reporting on Presentations and Discussions launched via Twitter.com @ The 2nd ANNUAL Sachs Cancer Bio Partnering & Investment Forum in Drug Development, 19th March 2014 • New York Academy of Sciences • USA

PCCI’s 7th Annual Roundtable “Crowdfunding for Life Sciences: A Bridge Over Troubled Waters?” May 12 2014 Embassy Suites Hotel, Chesterbrook PA 6:00-9:30 PM

CRISPR-Cas9 Discovery and Development of Programmable Genome Engineering – Gabbay Award Lectures in Biotechnology and Medicine – Hosted by Rosenstiel Basic Medical Sciences Research Center, 10/27/14 3:30PM Brandeis University, Gerstenzang 121

Tweeting on 14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

https://pharmaceuticalintelligence.com/press-coverage/

Statistical Analysis of Tweet Feeds from the 14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

What VCs Think about Your Pitch? Panel Summary of 1st Pitch Life Science Philly

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

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Track 9 Pharmaceutical R&D Informatics: Collaboration, Data Science and Biologics @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA

Aviva Lev-Ari, PhD, RN

 

April 30, 2014

 

Big Data and Data Science in R&D and Translational Research

10:50 Chairperson’s Remarks

Ralph Haffner, Local Area Head, Research Informatics, F. Hoffmann-La Roche AG

11:00 Can Data Science Save Pharmaceutical R&D?

Jason M. Johnson, Ph.D., Associate Vice President,

Scientific Informatics & Early Development and Discovery Sciences IT, Merck

Although both premises – that the viability of pharmaceutical R&D is mortally threatened and that modern “data science” is a relevant superhero – are

suspect, it is clear that R&D productivity is progressively declining and many areas of R&D suboptimally use data in decision-making. We will discuss

some barriers to our overdue information revolution, and our strategy for overcoming them.

11:30 Enabling Data Science in Externalized Pharmaceutical R&D

Sándor Szalma, Ph.D., Head, External Innovation, R&D IT,

Janssen Research & Development, LLC

Pharmaceutical companies have historically been involved in many external partnerships. With recent proliferation of hosted solutions and the availability

of cost-effective, massive high-performance computing resources there is an opportunity and a requirement now to enable collaborative data science. We

discuss our experience in implementing robust solutions and pre-competitive approaches to further these goals.

12:00 pm Co-Presentation: Sponsored by

Collaborative Waveform Analytics: How New Approaches in Machine Learning and Enterprise Analytics will Extend Expert Knowledge and Improve Safety Assessment

  • Tim Carruthers, CEO, Neural ID
  • Scott Weiss, Director, Product Strategy, IDBS

Neural ID’s Intelligent Waveform Service (IWS) delivers the only enterprise biosignal analysis solution combining machine learning with human expertise. A collaborative platform supporting all phases of research and development, IWS addresses a significant unmet need, delivering scalable analytics and a single interoperable data format to transform productivity in life sciences. By enabling analysis from BioBook (IDBS) to original biosignals, IWS enables users of BioBook to evaluate cardio safety assessment across the R&D lifecycle.

12:15 Building a Life Sciences Data

Sponsored by

Lake: A Useful Approach to Big Data

Ben Szekely, Director & Founding Engineer,

Cambridge Semantics

The promise of Big Data is in its ability to give us technology that can cope with overwhelming volume and variety of information that pervades R&D informatics. But the challenges are in practical use of disconnected and poorly described data. We will discuss: Linking Big Data from diverse sources for easy understanding and reuse; Building R&D informatics applications on top of a Life Sciences Data Lake; and Applications of a Data Lake in Pharma.

12:40 Luncheon Presentation I:

Sponsored by

Chemical Data Visualization in Spotfire

Matthew Stahl, Ph.D., Senior Vice President,

OpenEye Scientific Software

Spotfire deftly facilitates the analysis and interrogation of data sets. Domain specific data, such as chemistry, presents a set of challenges that general data analysis tools have difficulty addressing directly. Fortunately, Spotfire is an extensible platform that can be augmented with domain specific abilities. Spotfire has been augmented to naturally handle cheminformatics and chemical data visualization through the integration of OpenEye toolkits. The OpenEye chemistry extensions for Spotfire will be presented.

1:10 Luncheon Presentation II 

1:50 Chairperson’s Remarks

Yuriy Gankin, Ph.D., Co. Founder and CSO, GGA Software Services

1:55 Enable Translational Science by Integrating Data across the R&D Organization

Christian Gossens, Ph.D., Global Head, pRED Development Informatics Team,

pRED Informatics, F. Hoffmann-La Roche Ltd.

Multi-national pharmaceutical companies face an amazingly complex information management environment. The presentation will show that

a systematic system landscaping approach is an effective tool to build a sustainable integrated data environment. Data integration is not mainly about

technology, but the use and implementation of it.

2:25 The Role of Collaboration in Enabling Great Science in the Digital Age: The BARD Data Science Case Study

Andrea DeSouza, Director, Informatics & Data Analysis,

Broad Institute

BARD (BioAssay Research Database) is a new, public web portal that uses a standard representation and common language for organizing chemical biology data. In this talk, I describe how data professionals and scientists collaborated to develop BARD, organize the NIH Molecular Libraries Program data, and create a new standard for bioassay data exchange.

May 1. 2014

BIG DATA AND DATA SCIENCE IN R&D AND TRANSLATIONAL RESEARCH

10:30 Chairperson’s Opening Remarks

John Koch, Director, Scientific Information Architecture & Search, Merck

10:35 The Role of a Data Scientist in Drug Discovery and Development

Anastasia (Khoury) Christianson, Ph.D., Head, Translational R&D IT, Bristol-

Myers Squibb

A major challenge in drug discovery and development is finding all the relevant data, information, and knowledge to ensure informed, evidencebased

decisions in drug projects, including meaningful correlations between preclinical observations and clinical outcomes. This presentation will describe

where and how data scientists can support pharma R&D.

11:05 Designing and Building a Data Sciences Capability to Support R&D and Corporate Big Data Needs

Shoibal Datta, Ph.D., Director, Data Sciences, Biogen Idec

To achieve Biogen Idec’s strategic goals, we have built a cross-disciplinary team to focus on key areas of interest and the required capabilities. To provide

a reusable set of IT services we have broken down our platform to focus on the Ingestion, Digestion, Extraction and Analysis of data. In this presentation, we will outline how we brought focus and prioritization to our data sciences needs, our data sciences architecture, lessons learned and our future direction.

11:35 Data Experts: Improving Sponsored by

Translational Drug-Development Efficiency

Jamie MacPherson, Ph.D., Consultant, Tessella

We report on a novel approach to translational informatics support: embedding Data Experts’ within drug-project teams. Data experts combine first-line

informatics support and Business Analysis. They help teams exploit data sources that are diverse in type, scale and quality; analyse user-requirements and prototype potential software solutions. We then explore scaling this approach from a specific drug development team to all.

 

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