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Posts Tagged ‘Content Curation’


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

Curator: Stephen J. Williams, PhD.

Dr. Cathy N. Davidson from Duke University gives a talk entitled: Now You See It.  Why the Future of Learning Demands a Paradigm Shift

In this talk, shown below, Dr. Davidson shows how our current education system has been designed for educating students for the industrial age type careers and skills needed for success in the Industrial Age and how this educational paradigm is failing to prepare students for the challenges they will face in their future careers.

Or as Dr. Davidson summarizes

Designing education not for your past but for their future

As the video is almost an hour I will summarize some of the main points below

PLEASE WATCH VIDEO

Summary of talk

Dr. Davidson starts the talk with a thesis: that Institutions tend to preserve the problems they were created to solve.

All the current work, teaching paradigms that we use today were created for the last information age (19th century)

Our job to to remake the institutions of education work for the future not the one we inherited

Four information ages or technologies that radically changed communication

  1. advent of writing: B.C. in ancient Mesopotamia allowed us to record and transfer knowledge and ideas
  2. movable type – first seen in 10th century China
  3. steam powered press – allowed books to be mass produced and available to the middle class.  First time middle class was able to have unlimited access to literature
  4. internet- ability to publish and share ideas worldwide

Interestingly, in the early phases of each of these information ages, the same four complaints about the new technology/methodology of disseminating information was heard

  • ruins memory
  • creates a distraction
  • ruins interpersonal dialogue and authority
  • reduces complexity of thought

She gives an example of Socrates who hated writing and frequently stated that writing ruins memory, creates a distraction, and worst commits ideas to what one writes down which could not be changed or altered and so destroys ‘free thinking’.

She discusses how our educational institutions are designed for the industrial age.

The need for collaborative (group) learning AND teaching

Designing education not for your past but for the future

In other words preparing students for THEIR future not your past and the future careers that do not exist today.

In the West we were all taught to answer silently and alone.  However in Japan, education is arranged in the han or group think utilizing the best talents of each member in the group.  In Japan you are arranged in such groups at an early age.  The concept is that each member of the group contributes their unique talent and skill for the betterment of the whole group.  The goal is to demonstrate that the group worked well together.

see https://educationinjapan.wordpress.com/education-system-in-japan-general/the-han-at-work-community-spirit-begins-in-elementary-school/ for a description of “in the han”

In the 19th century in institutions had to solve a problem: how to get people out of the farm and into the factory and/or out of the shop and into the firm

Takes a lot of regulation and institutionalization to convince people that independent thought is not the best way in the corporation

keywords for an industrial age

  • timeliness
  • attention to task
  • standards, standardization
  • hierarchy
  • specialization, expertise
  • metrics (measures, management)
  • two cultures: separating curriculum into STEM versus artistic tracts or dividing the world of science and world of art

This effort led to a concept used in scientific labor management derived from this old paradigm in education, an educational system controlled and success measured using

  • grades (A,B,C,D)
  • multiple choice tests

keywords for our age

  • workflow
  • multitasking attention
  • interactive process (Prototype, Feedback)
  • data mining
  • collaboration by difference

Can using a methodology such as scientific curation affect higher education to achieve this goal of teaching students to collaborate in an interactive process using data mining to create a new workflow for any given problem?  Can a methodology of scientific curation be able to affect such changes needed in academic departments to achieve the above goal?

This will be the subject of future curations tested using real-world in class examples.

However, it is important to first discern that scientific content curation takes material from Peer reviewed sources and other expert-vetted sources.  This is unique from other types of content curation in which take from varied sources, some of which are not expert-reviewed, vetted, or possibly ‘fake news’ or highly edited materials such as altered video and audio.  In this respect, the expert acts not only as curator but as referee.  In addition, collaboration is necessary and even compulsory for the methodology of scientific content curation, portending the curator not as the sole expert but revealing the CONTENT from experts as the main focus for learning and edification.

Other article of note on this subject in this Open Access Online Scientific Journal include:

The above articles will give a good background on this NEW Conceived Methodology of Scientific Curation and its Applicability in various areas such as Medical Publishing, and as discussed below Medical Education.

To understand the new paradigm in medical communication and the impact curative networks have or will play in this arena please read the following:

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

This article discusses a history of medical communication and how science and medical communication initially moved from discussions from select individuals to the current open accessible and cooperative structure using Web 2.0 as a platform.

 

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Analysis of Utilizing LPBI Group’s Scientific Curation Platform as an Educational Tool: New Paradigm for Student Engagement

Author: Stephen J. Williams, Ph.D.

 

 

Use of LBPI Platform for Educational Purposes

Goal:  to offer supplemental information for student lessons in an upper level Biology course on Cell Signaling and Cell Motility with emphasis on disease etiology including cancer, neurological disease, and cardiovascular disease.

Course:  Temple University Department of Biology course Cell Signaling and Motility Spring semester 2019. Forty five students enrolled.

Methodology:  Each weekly lesson was presented to students as a PowerPoint presentation.  After each lesson the powerpoint presentation was originally meant to be disseminated to each class-registered student on the students Canvas account.  Canvas is a cloud based Learning Management Software developed by educational technology company Salt Lake City, Utah company Infrastructure, Inc.  According to rough figures, Canvas® charges a setup fee and at least $30 per user (for a university the size of Temple University: 55,000 students at $30 each = 1.6 million a semester for user fees only).

As a result of a technical issue with uploading the first week lesson on this system, I had informed the class that, as an alternative means, class presentation notes and lectures will be posted on the site www.pharmaceuticalintelligence.com as a separate post and searchable on all search engines including Google, Twitter, Yahoo, Bing, Facebook etc. In addition, I had informed the students that supplemental information, from curated posts and articles from our site, would be added to the class lecture post as supplemental information they could use for further reading on the material as well as helpful information and reference for class projects.

The posted material was tagged with #TUBiol3373 (university abbreviation, department, course number) and disseminated to various social media platforms using our system.  This allowed the students to enter #TUBiol3373 in any search engine to easily find their lecture notes and supplemental information.

This gave students access to lectures on a mobile platform which was easily discoverable due to our ability to do search engine optimization. (#TUBiol3373 was among the first search results on most popular search engines).

From a technical standpoint,  the ease at which posts of this nature can be made as well as the ease of including links to full articles as references as well as media has been noted.  Although students seem to navigate the Canvas software with ease, they had noticed many professors have issues or problems with using this software, especially with navigating the software for their needs.   LBPI’s platform is an easily updated, accessible, and extensive knowledge system which can alleviate many of these technical issues and provide the added value of incorporating media based instructional material as well as downloadable file and allow the instructor ability to expound on the presented material with commentary.  In addition due to the social nature of the platform, feedback can be attained by use of curated site statistics and commentary sections as well as online surveys.

 

Results

After the first week, all 45 students used LBPI platform to access these lecture notes with 17 out of 45 continuing to refer to the site during every week (week 1-4) to the class notes.  This was evident from our site statistics as well as number of downloads of the material.  The students had used the #TUBIol3373 and were directed to the site mainly from search engines Google and Yahoo.  In addition, students had also clicked on the links corresponding to supplemental information which I had included, from articles on our site.  In addition, because of the ability to incorporate media on our site, additional information including instructional videos and interviews were included in lecture posts, and this material was easily updated on the instructor’s side.

Adoption of the additional material from our site was outstanding, as many students had verbally said that the additional material was very useful in their studies.  This was also evidenced by site statistics owing to the secondary clicks made from the class lecture post going to additional articles, some not even included as links on the original post.

In addition, and  more important, students had incorporated many of the information from the additional site articles posted and referenced in their class group projects.  At end of semester a survey was emailed to each student  to assess the usefulness of such a teaching strategy. Results of the polling are shown below.

Results from polling of students of #TUBiol3373 “Cell Signaling & Motility” Class

Do you find using a web based platform such as a site like this an easier communication platform for posting lecture notes/added information than a platform like Canvas®? (5 votes)

Answer Votes Percent  
Yes 2 40%  
Somewhat but could use some improvement 2 40%  
No 1 20%  
Did not use web site 0 0%  

 

Do you find using an open access, curated information platform like this site more useful than using multiple sources to find useful extra study/presentation materials? (6 votes)

Answer Votes Percent  
Yes 5 83%  
No 1 17%  

 

Did you use the search engine on the site (located on the top right of the home page) to find extra information on topics for your presentations/study material? (5 votes)

Answer Votes Percent  
Yes 4 67%  
No 1 17%  
Did not use web site 1 17%  

 

Were you able to easily find the supplemental information for each lecture on search engines like Google/Yahoo/Bing/Twitter using the hashtag #TUBiol3373? (6 votes)

Answer Votes Percent  
Yes I was able to find the site easily 4 67%  
No 1 17%  
Did not use a search engine to find site, went directly to site 1 17%  
Encountered some difficulty 0 0%  
Did not use the site for supplemental or class information 0 0%  

 

How did you find the supplemental material included on this site above the Powerpoint presented material for each of the lectures? (7 votes)

Answer Votes Percent  
Very Useful 4 57%  
Did not use supplemental information 2 29%  
Somewhat Useful 1 14%  
Not Useful 0 0%  

How many times did you use the information on this site (https://www.pharmaceuticalintelligence.com) for class/test/project preparation? (7 votes)

Answer Votes Percent  
Frequently 3 43%  
Sparingly 2 29%  
Occasionally 1 14%  
Never 1 14%  

 

 

 

 

 

 

 

Views of #TUBiol3373 lessons/posts on www.pharmaceuticalintelligence.com                    

 

Lesson/Title Total # views # views 1st day # views 2nd day % views day 1 and 2 % views  after 1st 2 days
Lesson 1 AND 2 Cell Signaling & Motility: Lessons, Curations and Articles of reference as supplemental information: #TUBiol3373 60 27 15 93% 45%
Lesson 3 Cell Signaling And Motility: G Proteins, Signal Transduction: Curations and Articles of reference as supplemental information: #TUBiol3373 56 12 11 51% 93%
Lesson 4 Cell Signaling And Motility: G Proteins, Signal Transduction: Curations and Articles of reference as supplemental information: #TUBiol3373 37 17 6 48% 31%
Lesson 5 Cell Signaling And Motility: Cytoskeleton & Actin: Curations and Articles of reference as supplemental information: #TUBiol3373 13 6 2 17% 15%
Lesson 8 Cell Signaling and Motility: Lesson and Supplemental Information on Cell Junctions and ECM: #TUBiol3373 16 8 2 22% 13%
Lesson 9 Cell Signaling: Curations and Articles of reference as supplemental information for lecture section on WNTs: #TUBioll3373 20 10 3 28% 15%
Curation of selected topics and articles on Role of G-Protein Coupled Receptors in Chronic Disease as supplemental information for #TUBiol3373 19 11 2 28% 13%
Lesson 10 on Cancer, Oncogenes, and Aberrant Cell Signal Termination in Disease for #TUBiol3373 21 10 2 26% 20%
Totals 247 69 46 31% 62%
           

 

Note: for calculation of %views on days 1 and 2 of posting lesson and supplemental material on the journal; %views day1 and 2 = (#views day 1 + #views day 2)*100/45 {45 students in class}

For calculation of %views past day 1 and 2 = (total # views – day1 views – day2 views) * 100/45

For calculation in total column last two columns were divided by # of students (45) and # of posts (8)

 

Overall class engagement was positive with 31% of students interacting with the site during the course on the first two days after posting lessons while 61% of students interacted with the site during the rest of the duration of the course.  The higher number of students interacting with the site after the first two days after lecture and posting may be due to a higher number of students using the posted material for study for the test and using material for presentation purposes.

Engagement with the site for the first two days post lecture ranged from 93% engagement to 22% engagement.  As the class neared the first exam engagement with the site was high however engagement was lower near the end of the class period potentially due to the last exam was a group project and not a written exam.  Students appeared to engage highly with the site to get material for study for the written exam however there still was significant engagement by students for purposes of preparation for oral group projects.  Possibly engagement with the site post 2 days for the later lectures could be higher if a written exam was also given towards the end of the class as well.  This type of analysis allows the professor to understand the level of class engagement week by week.

The results of post-class polling confirm some of the conclusions on engagement.  After the final grades were given out all 45 students received an email with a link to the poll.  Of the 45 students emailed, there were 20 views of the poll with 5-7 answers per question.  Interestingly, most answers were positive on the site and the use of curated material for learning and a source of research project material.   It was very easy finding the posts using the #classname and most students used Google to find the material, which was at the top of Google search results.  Not many students used Twitter or other search engines.  Some went directly to the site.  A majority (71%) found the material useful or somewhat useful for their class presentations and researching topics.

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Lesson 1 & 2 Cell Signaling & Motility: Lessons, Curations and Articles of reference as supplemental information: #TUBiol3373

Curator: Stephen J. Williams, Ph.D.

UPDATED 2/05/2019

UPDATED 1/27/2020

Syllabus for Cell Signaling & Motility for 2020

CELL SIGNALING AND MOTILITY (BIOL 3373)

SPRING 2020

Lectures:

Monday 5:00 PM – 8:00 PM

Biology Life Sciences, Room 342

Instructor:

Antonio Giordano, M.D., Ph.D.

Co-Instructor:

Stephen J. Williams, PhD

email: sjwilliamspa@comcast.net or tug83586@temple.edu

on Twitter @StephenJWillia2

Office hours: Biology Life Sciences Building, Room 431.

Friday: 12:00 noon – 2:00 PM. By appointment

(Phone: 215-2049520, or email: giordano@temple.edu).

Prerequisite:

BIO 3096, Cell Structure and Function (Minimum Grade of C- | May not be taken concurrently). 

Description:

The communication among cells is essential for the regulation of the development of an organism and for the control of its physiology and homeostasis. Aberrant cellular signaling events are often associated with human pathological conditions, such as cancer, neurological disorders, cardiovascular diseases and so on. The full characterization of cell signaling systems may provide useful insights into the pathogenesis of several human maladies.

Text:

Molecular Biology of the Cell 6th Edition, Alberts et al. Garland Science. This textbook is available at the Temple Bookstore.

Grading:

The final grade will be based on the score of four examinations that include both group and individuals assignment. Each exam accounts for 25% of the final grade. There will be no make-up tests during the course. If you have a documented medical excuse and you contact me as soon as possible after the emergency, I will arrange a make-up exam. Complaints regarding the grading will not be considered later than two weeks after the test is returned.

Blackboard:

Announcements will be readily posted on Blackboard. It is your responsibility to check Blackboard periodically.

Attendance: Lecture attendance is mandatory. In addition, punctuality is expected.

Disabilities: Students with documented disabilities who need particular accommodation should contact me privately as soon as possible.

Honesty and Civility:

Students must follow the Temple’s Code of Conduct (see http://www.temple.edu/assistance/udc/coc.htm). This Code of Conduct prohibits: 1. Academic dishonesty and impropriety, including plagiarism and cheating. 2. Interfering or attempting to interfere with or disrupting the conduct of classes or any other activity of the University.”

Academic Rights and Responsibilities:

The policy of the University that regulates Student and Faculty Academic Rights and Responsibilities (Policy # 03.70.02) is available at the following web link: http://policies.temple.edu/getdoc.asp?policy_no=03.70.02

This policy sets the parameters for freedom to learn and freedom to teach, which constitute the pillars of academia.

 

SCHEDULE

This schedule is a general outline, which may be eventually modified. Changes will be announced in advance. Please, always check Blackboard and your email.

Date Topic
Jan 13 Introduction (course overview  and discussion of syllabus). General concepts: Eukaryotic and prokaryotic cell; DNA, RNA  and proteins: Protein synthesis
Jan 20 Martin Luther King, Jr. Day (no classes held)
Jan 27 DNA analysis, RNA analysis; Proteins analysis; Microscopy.
Feb 3 Signaling: general concepts; Introduction to G-proteins; signaling via G-proteins (1)
Feb 10 Exam 1: In class presentation (group assignment)
Feb 17 Signaling via G-proteins (2); tyrosine kinase receptors signaling; Ras-MAPK pathway.
Feb 24 Exam 2: In class presentation (group assignment)
March 2- 8 Spring break
Mar 9

 

Cytoskeleton:  Intermediate filaments; actin
Mar 16 Cytoskeleton: actin binding proteins; microtubules
Mar 23

 

Cytoskeleton: microtubules
Mar 30

 

Exam 3: in class Multiple choice questions (individual assignment)
Apr 6 Extracellular matrix; cell adhesion; coordinated polarization.
Apr  13 Cell motility and Wnt Signal Signaling. 
Apr  20 Medical consequences of aberrant signaling pathways; production of small molecules for protein kinases In cancer therapy.
Study days
May 4 Exam 4: In class presentation (group assignment)

 

Below is Powerpoint presentations for Lesson 1 and Lesson 2.  Please check for UPDATES on this page for additional supplemental information for these Lessons including articles from this Online Access Journal

 

cell signaling and motility 1 lesson

 

cell signaling and motility 2 lesson

The following articles and curations discuss about the new paradigm how we now envision DNA, in particular how we now understand that the important parts of the genome are not just the exons which code for proteins but also the intronic DNA, which contains all the regulatory elements such as promoters, lncDNA, miRNA sequences etc.  These are good reads for your presentations.

The Search for the Genetic Code

Junk DNA codes for valuable miRNAs

 

And on How the Cell Creates Diversity post the Genetic Code by Use of Post Translational Modifications to Bring Diversity to Protein Structure/Function

Expanding the Genetic Alphabet and Linking the Genome to the Metabolome

Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

Also there is a link to a Blood article using FISH to detect gene amplifications after Gleevec resistance onset here

Novel Mechanisms of Resistance to Novel Agents

Some additional videos on some of the techniques we had covered

Southern Blotting (View Video)

Restriction Fragment  Length Polymorphism (View Video) [RFLP]

Far Western Blotting Procedure 

 

Other Articles related to the #TUBiol3373 course include:

Lesson 9 Cell Signaling: Curations and Articles of reference as supplemental information for lecture section on WNTs: #TUBioll3373

Curation of selected topics and articles on Role of G-Protein Coupled Receptors in Chronic Disease as supplemental information for #TUBiol3373

 

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Twitter, Google, LinkedIn Enter in the Curation Foray: What’s Up With That?

 

Reporter: Stephen J. Williams, Ph.D.

Recently Twitter has announced a new feature which they hope to use to increase engagement on their platform. Originally dubbed Project Lightning and now called Moments, this feature involves many human curators which aggregate and curate tweets surrounding individual live events(which used to be under #Live).

As Madhu Muthukumar (@justmadhu), Twitter’s Product Manager, published a blog post describing Moments said:

“Every day, people share hundreds of millions of tweets. Among them are things you can’t experience anywhere but on Twitter: conversations between world leaders and celebrities, citizens reporting events as they happen, cultural memes, live commentary on the night’s big game, and many more,” the blog post noted. “We know finding these only-on-Twitter moments can be a challenge, especially if you haven’t followed certain accounts. But it doesn’t have to be.”

Please see more about Moments on his blog here.

Moments is a new tab on Twitter’s mobile and desktop home screens where the company will curate trending topics as they’re unfolding in real-time — from citizen-reported news to cultural memes to sports events and more. Moments will fall into five total categories, including “Today,” “News,” “Sports,” “Entertainment” and “Fun.” (Source: Fox)

Now It’s Google’s Turn

 

As Dana Blankenhorn wrote in his article Twitter, Google Try It Buzzfeed’s Way With Curation

in SeekingAlpha

What’s a challenge for Google is a direct threat to Twitter’s existence.

For all the talk about what doesn’t work in journalism, curation works. Following the news, collecting it and commenting, and encouraging discussion, is the “secret sauce” for companies like Buzzfeed, Vox, Vice and The Huffington Post, which often wind up getting more traffic from a story at, say The New York Times (NYSE:NYT), than the Times does as a result.

Curation is, in some ways, a throwback to the pre-Internet era. It’s done by people. (At least I think I’m a people.) So as odd as it is for Twitter (NYSE:TWTR) to announce it will curate live events it’s even odder to see Google (NASDAQ:GOOG) (NASDAQ:GOOGL) doing it in a project called YouTube Newswire.

Buzzfeed, Google’s content curation platform, made for desktop as well as a mobile app, allows sharing of curated news, viral videos.

The feel for both Twitter and Google’s content curation will be like a newspaper, with an army of human content curators determining what is the trendiest news to read or videos to watch.

BuzzFeed articles, or at least, the headlines can easily be mined from any social network but reading the whole article still requires that you open the link within the app or outside using a mobile web browser. Loading takes some time–a few seconds longer. Try browsing the BuzzFeed feed on the app and you’ll notice the obvious difference.

However it was earlier this summer in a Forbes article Why Apple, Snapchat and Twitter are betting on human editors, but Facebook and Google aren’t that Apple, Snapchat and Twitter as well as LinkedIn Pulse and Instragram were going to use human editors and curators while Facebook and Google were going to rely on their powerful algorithms. Google (now Alphabet) CEO Eric Schmidt has even called Apple’s human curated playlists “elitist” although Google Play has human curated playlists.

Maybe Google is responding to views on its Google News like this review in VentureBeat:

Google News: Less focused on social signals than textual ones, Google News uses its analytic tools to group together related stories and highlight the biggest ones. Unlike Techmeme, it’s entirely driven by algorithms, and that means it often makes weird choices. I’ve heard that Google uses social sharing signals from Google+ to help determine which stories appear on Google News, but have never heard definitive confirmation of that — and now that Google+ is all but dead, it’s mostly moot. I find Google News an unsatisfying home page, but it is a good place to search for news once you’ve found it.

Now WordPress Too!

 

WordPress also has announced its curation plugin called Curation Traffic.

According to WordPress

You Own the Platform, You Benefit from the Traffic

“The Curation Traffic™ System is a complete WordPress based content curation solution. Giving you all the tools and strategies you need to put content curation into action.

It is push-button simple and seamlessly integrates with any WordPress site or blog.

With Curation Traffic™, curating your first post is as easy as clicking “Curate” and the same post that may originally only been sent to Facebook or Twitter is now sent to your own site that you control, you benefit from, and still goes across all of your social sites.”

The theory the more you share on your platform the more engagement the better marketing experience. And with all the WordPress users out there they have already an army of human curators.

So That’s Great For News But What About Science and Medicine?

 

The news and trendy topics such as fashion and music are common in most people’s experiences. However more technical areas of science, medicine, engineering are not in most people’s domain so aggregation of content needs a process of peer review to sort basically “the fact from fiction”. On social media this is extremely important as sensational stories of breakthroughs can spread virally without proper vetting and even influence patient decisions about their own personal care.

Expertise Depends on Experience

In steps the human experience. On this site (www.pharmaceuticalintelligence.com) we attempt to do just this. A consortium of M.D.s, Ph.D. and other medical professionals spend their own time to aggregate not only topics of interest but curate on specific topics to add some more insight from acceptable sources over the web.

In Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison; Dr. Larry Berstein compares a museum or music curator to curation of scientific findings and literature and draws similar conclusions from each: that a curation can be a tool to gain new insights previously unseen an observer. A way of stepping back to see a different picture, hear a different song.

 

For instance, using a Twitter platform, we curate #live meeting notes and tweets from meeting attendees (please see links below and links within) to give a live conference coverage

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

and curation and analysis give rise not only to meeting engagement butunique insights into presentations.

 

In addition, the use of a WordPress platform allows easy sharing among many different social platforms including Twitter, Google+, LinkedIn, Pinterest etc.

Hopefully, this will catch on to the big powers of Twitter, Google and Facebook to realize there exists armies of niche curation communities which they can draw on for expert curation in the biosciences.

Other posts on this site on Curation and include

 

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

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

Stem Cells and Cardiac Repair: Content Curation & Scientific Reporting

Cardiovascular Diseases and Pharmacological Therapy: Curations

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

 

 

 

 

 

 

 

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Mozilla Science Lab Promotes Data Reproduction Through Open Access: Report from 9/10/2015 Online Meeting

Reporter: Stephen J. Williams, Ph.D.

Mozilla Inc. is developing a platform for scientists to discuss the issues related to developing a framework to share scientific data as well as tackle the problems of scientific reproducibility in an Open Access manner. According to their blog

https://blog.mozilla.org/blog/2013/06/14/5992/

We’re excited to announce the launch of the Mozilla Science Lab, a new initiative that will help researchers around the world use the open web to shape science’s future.

Scientists created the web — but the open web still hasn’t transformed scientific practice to the same extent we’ve seen in other areas like media, education and business. For all of the incredible discoveries of the last century, science is still largely rooted in the “analog” age. Credit systems in science are still largely based around “papers,” for example, and as a result researchers are often discouraged from sharing, learning, reusing, and adopting the type of open and collaborative learning that the web makes possible.

The Science Lab will foster dialog between the open web community and researchers to tackle this challenge. Together they’ll share ideas, tools, and best practices for using next-generation web solutions to solve real problems in science, and explore ways to make research more agile and collaborative.

On their blog they highlight various projects related to promoting Open Access for scientific data

On September 10, 2015 Mozilla Science Lab had their scheduled meeting on scientific data reproduce ability.  The meeting was free and covered by ethernet and on social media. The Twitter hashtag for updates and meeting discussion is #mozscience (https://twitter.com/search?q=%23mozscience )

Open Access Meeting Announcement on Twitter

https://twitter.com/MozillaScience/status/641642491532283904

//platform.twitter.com/widgets.js

mozilla science lab

Mozilla Science Lab @MozillaScience

Join @khinsen @abbycabs + @EvoMRI tmrw (11AM ET) to hear about replication, publishing + #openscience. Details: https://etherpad.mozilla.org/sciencelab-calls-sep10-2015 …

AGENDA:

  • Mozilla Science Lab Updates
  • Staff welcomes and thank yous:
  • Welcoming Zannah Marsh, our first Instructional Designer
  • Workshopping the “Working Open” guide:
    • Discussion of Future foundation and GitHub projects
    • Discussion of submission for open science project funding
  • Contributorship Badges Pilot – an update! – Abby Cabunoc Mayes – @abbycabs
  • Will be live on GigaScience September 17th!
  • Where you can jump in: https://github.com/mozillascience/paperbadger/issues/17
  • Questions regarding coding projects – Abby will coordinate efforts on coding into their codebase
  • The journal will publish and authors and reviewers get a badge and their efforts and comments will appear on GigaScience: Giga Science will give credit for your reviews – supports an Open Science Discussion

Roadmap for

  • Fellows review is in full swing!
  • MozFest update:
  • Miss the submission deadline? You can still apply to join our Open Research Accelerator and join us for the event (PLUS get a DOI for your submission and 1:1 help)

A discussion by Konrad Hinsen (@khinsen) on ReScience, a journal focused on scientific replication will be presented:

  • ReScience – a new journal for replications – Konrad Hinsen @khinsen
  • ReScience is dedicated to publishing replications of previously published computational studies, along with all the code required to replicate the results.
  • ReScience lives entirely on GitHub. Submissions take the form of a Git repository, and review takes place in the open through GitHub issues. This also means that ReScience is free for everyone (authors, readers, reviewers, editors… well, I said everyone, right?), as long as GitHub is willing to host it.
  • ReScience was launched just a few days ago and is evolving quickly. To stay up to date, follow @ReScienceEds on Twitter. If you want to volunteer as a reviewer, please contact the editorial board.

The ReScience Journal Reproducible Science is Good. Replicated Science is better.

ReScience is a peer-reviewed journal that targets computational research and encourages the explicit reproduction of already published research promoting new and open-source implementations in order to ensure the original research is reproducible. To achieve such a goal, the whole editing chain is radically different from any other traditional scientific journal. ReScience lives on github where each new implementation is made available together with the comments, explanations and tests. Each submission takes the form of a pull request that is publicly reviewed and tested in order to guarantee any researcher can re-use it. If you ever reproduced computational result from the literature, ReScience is the perfect place to publish this new implementation. The Editorial Board

Notes from his talk:

– must be able to replicate paper’s results as written according to experimental methods

– All authors on ReScience need to be on GitHub

– not accepting MatLab replication; replication can involve computational replication;

  • Research Ideas and Outcomes Journal – Daniel Mietchen @EvoMRI
    • Postdoc at Natural Museum of London doing data mining; huge waste that 90% research proposals don’t get used so this journal allows for publishing proposals
    • Learned how to write proposals by finding a proposal online open access
    • Reviewing system based on online reviews like GoogleDocs where people view, comment
    • Growing editorial and advisory board; venturing into new subject areas like humanities, economics, biological research so they are trying to link diverse areas under SOCIAL IMPACT labeling
    • BIG question how to get scientists to publish their proposals especially to improve efficiency of collaboration and reduce too many duplicated efforts as well as reagent sharing
    • Crowdfunding platform used as post publication funding mechanism; still in works
    • They need a lot of help on the editorial board so if have a PhD PLEASE JOIN
  • Website:
  • Background:
  • Science article:
  • Some key features:
  • for publishing all steps of the research cycle, from proposals (funded and not yet funded) onwards
  • maps submissions to societal challenges
  • focus on post-publication peer review; pre-submission endorsement; all reviews public
  • lets authors choose which publishing services they want, e.g. whether they’d like journal-mediated peer review
  • collaborative WYSIWYG authoring and publishing platform based on JATS XML

A brief discussion of upcoming events on @MozillaScience

Meetings are held 2nd Thursdays of each month

Additional plugins, coding, and new publishing formats are available at https://www.mozillascience.org/

Other related articles on OPEN ACCESS Publishing were published in this Open Access Online Scientific Journal, include the following:

Archives of Medicine (AOM) to Publish from “Leaders in Pharmaceutical Business Intelligence (LPBI)” Open Access On-Line Scientific Journal http://pharmaceuticalintelligence.com

Annual Growth in NIH Clicks: 32% Open Access Online Scientific Journal http://pharmaceuticalintelligence.com

Collaborations and Open Access Innovations – CHI, BioIT World, 4/29 – 5/1/2014, Seaport World Trade Center, Boston

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

Reconstructed Science Communication for Open Access Online Scientific Curation

The Fatal Self Distraction of the Academic Publishing Industry: The Solution of the Open Access Online Scientific Journals

 

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Leaders in Pharmaceutical Business Intelligence would like to announce their First Volume of their BioMedical E-Book Series A: eBooks on Cardiovascular Diseases

 

Perspectives on Nitric Oxide in Disease Mechanisms

Nitric Oxide coverwhich is now available on Amazon Kindle at

http://www.amazon.com/dp/B00DINFFYC

This book is a comprehensive review of Nitric Oxide, its discovery, function, and related opportunities for Targeted Therapy written by  Experts, Authors, Writers.  This book is a series of articles delineating the basic functioning of the NOS isoforms, their production widely by endothelial cells, and the effect of NITRIC OXIDE production by endothelial cells, by neutrophils and macrophages, the effect on intercellular adhesion, and the effect of circulatory shear and turbulence on NITRIC OXIDE production. The e-Book’s articles have been published on the  Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published, in real time in the e-Book which is a live book.

 

We invite e-Readers to write an Article Reviews on Amazon for this e-Book.

 

All forthcoming BioMed e-Book Titles can be viewed at:

https://pharmaceuticalintelligence.com/biomed-e-books/

 

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

  • delivering curation and summary interpretations of latest findings and innovations on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
  • providing a social platform for scientists and clinicians to enter into discussion using social media
  • compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

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.

Table of Contents for Perspectives on Nitric Oxide in Disease Mechanisms

Chapter 1: Nitric Oxide Basic Research

Chapter 2: Nitric Oxide and Circulatory Diseases

Chapter 3: Therapeutic Cardiovascular Targets

Chapter 4: Nitric Oxide and Neurodegenerative Diseases

Chapter 5: Bone Metabolism

Chapter 6: Nitric Oxide and Systemic Inflammatory Disease

Chapter 7: Nitric Oxide: Lung and Alveolar Gas Exchange

Chapter 8. Nitric Oxide and Kidney Dysfunction

Chapter 9: Nitric Oxide and Cancer 

 

 

 

 

 

 

 

 

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Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle


Cancer Biology and Genomics for Disease Diagnosis (Vol. I) Now Available for Amazon Kindle

Reporter: Stephen J Williams, PhD

Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series C: e-Books on Cancer & Oncology

Volume One: Cancer Biology and Genomics for Disease Diagnosis

CancerandOncologyseriesCcoverwhich is now available on Amazon Kindle at                          http://www.amazon.com/dp/B013RVYR2K.

This e-Book is a comprehensive review of recent Original Research on Cancer & Genomics including related opportunities for Targeted Therapy written by Experts, Authors, Writers. This ebook highlights some of the recent trends and discoveries in cancer research and cancer treatment, with particular attention how new technological and informatics advancements have ushered in paradigm shifts in how we think about, diagnose, and treat cancer. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates.

We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon. All forthcoming BioMed e-Book Titles can be viewed at:

https://pharmaceuticalintelligence.com/biomed-e-books/

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

  • delivering curation and summary interpretations of latest findings and innovations
  • on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
  • providing a social platform for scientists and clinicians to enter into discussion using social media
  • compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

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.

Table of Contents for Cancer Biology and Genomics for Disease Diagnosis

Preface

Introduction  The evolution of cancer therapy and cancer research: How we got here?

Part I. Historical Perspective of Cancer Demographics, Etiology, and Progress in Research

Chapter 1:  The Occurrence of Cancer in World Populations

Chapter 2.  Rapid Scientific Advances Changes Our View on How Cancer Forms

Chapter 3:  A Genetic Basis and Genetic Complexity of Cancer Emerge

Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth

Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure

Part II. Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development

Chapter 6:  Treatment Strategies

Chapter 7:  Personalized Medicine and Targeted Therapy

Part III.Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection

Chapter 8:  Diagnosis                                     

Chapter 9:  Detection

Chapter 10:  Biomarkers

Chapter 11:  Imaging In Cancer

Chapter 12: Nanotechnology Imparts New Advances in Cancer Treatment, Detection, &  Imaging                                 

Epilogue by Larry H. Bernstein, MD, FACP: Envisioning New Insights in Cancer Translational Biology

 

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Leaders in Pharmaceutical Intelligence Presentation at The Life Sciences Collaborative

Curator: Stephen J. Williams, Ph.D. Website Analytics: Adam Sonnenberg, BSc Leaders in Pharmaceutical Intelligence presented their ongoing efforts to develop an open-access scientific and medical publishing and curation platform to The Life Science Collaborative, an executive pharmaceutical and biopharma networking group in the Philadelphia/New Jersey area.

Our Team

Slide1

For more information on the Vision, Funding Deals and Partnerships please see our site at https://pharmaceuticalintelligence.com/vision/

Slide2

For more information about our Team please see our site at https://pharmaceuticalintelligence.com/contributors-biographies/

Slide5

For more information of LPBI Deals and Partnerships please see our site at https://pharmaceuticalintelligence.com/joint-ventures/

Slide4

For more information about our BioMed E-Series please see our site at https://pharmaceuticalintelligence.com/biomed-e-books/

E-Book Titles by LPBI

LPBI book titles slide Slide8Slide3

Slide6

For more information on Real-Time Conference Coverage including a full list of Conferences Covered by LPBI please go to https://pharmaceuticalintelligence.com/press-coverage/

For more information on Real-Time Conference Coverage and a full listing of Conferences Covered by LPBI please go to:

https://pharmaceuticalintelligence.com/press-coverage/ Slide7

Slide10

The Pennsylvania (PA) and New Jersey (NJ) Biotech environment had been hit hard by the recession and loss of anchor big pharma companies however as highlighted by our interviews in “The Vibrant Philly Biotech Scene” and other news outlets, additional issues are preventing the PA/NJ area from achieving its full potential (discussions also with LSC)

Slide9Download the PowerPoint slides here: Presentationlsc

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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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10:15AM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

10:15 a.m. Panel Discussion — IT/Big Data

IT/Big Data

The human genome is composed of 6 billion nucleotides (using the genetic alphabet of T, C, G and A). As the cost of sequencing the human genome is decreasing at a rapid rate, it might not be too far into the future that every human being will be sequenced at least once in their lifetime. The sequence data together with the clinical data are going to be used more and more frequently to make clinical decisions. If that is true, we need to have secure methods of storing, retrieving and analyzing all of these data.  Some people argue that this is a tsunami of data that we are not ready to handle. The panel will discuss the types and volumes of data that are being generated and how to deal with it.

IT/Big Data

   Moderator:

Amy Abernethy, M.D.
Chief Medical Officer, Flatiron

Role of Informatics, SW and HW in PM. Big data and Healthcare

How Lab and Clinics can be connected. Oncologist, Hematologist use labs in clinical setting, Role of IT and Technology in the environment of the Clinicians

Compare Stanford Medical Center and Harvard Medical Center and Duke Medical Center — THREE different models in Healthcare data management

Create novel solutions: Capture the voice of the patient for integration of component: Volume, Veracity, Value

Decisions need to be made in short time frame, documentation added after the fact

No system can be perfect in all aspects

Understanding clinical record for conversion into data bases – keeping quality of data collected

Key Topics

Panelists:

Stephen Eck, M.D., Ph.D.
Vice President, Global Head of Oncology Medical Sciences,
Astellas, Inc.

Small data expert, great advantage to small data. Populations data allows for longitudinal studies,

Big Mac Big Data – Big is Good — Is data been collected suitable for what is it used, is it robust, limitations, of what the data analysis mean

Data analysis in Chemical Libraries – now annotated

Diversity data in NOTED by MDs, nuances are very great, Using Medical Records for building Billing Systems

Cases when the data needed is not known or not available — use data that is available — limits the scope of what Valuable solution can be arrived at

In Clinical Trial: needs of researchers, billing clinicians — in one system

Translation of data on disease to data object

Signal to Noise Problem — Thus Big data provided validity and power

 

J. Michael Gaziano, M.D., M.P.H., F.R.C.P.
Scientific Director, Massachusetts Veterans Epidemiology Research
and Information Center (MAVERIC), VA Boston Healthcare System;
Chief Division of Aging, Brigham and Women’s Hospital;
Professor of Medicine, Harvard Medical School

at BWH since 1987 at 75% – push forward the Genomics Agenda, VA system 25% – VA is horizontally data integrated embed research and knowledge — baseline questionnaire 200,000 phenotypes – questionnaire and Genomics data to be integrated, Data hierarchical way to be curated, Simple phenotypes, validate phenotypes, Probability to have susceptibility for actual disease, Genomics Medicine will benefit Clinicians

Data must be of visible quality, collect data via Telephone VA – on Med compliance study, on Ability to tolerate medication

–>>Annotation assisted in building a tool for Neurologist on Alzheimer’s Disease (AlzSWAN knowledge base) (see also Genotator , a Disease-Agnostic Tool for Annotation)

–>>Curation of data is very different than statistical analysis of Clinical Trial Data

–>>Integration of data at VA and at BWH are tow different models of SUCCESSFUL data integration models, accessing the data is also using a different model

–>>Data extraction from the Big data — an issue

–>>Where the answers are in the data, build algorithms that will pick up causes of disease: Alzheimer’s – very difficult to do

–>>system around all stakeholders: investment in connectivity, moving data, individual silo, HR, FIN, Clinical Research

–>>Biobank data and data quality

 

Krishna Yeshwant, M.D.
General Partner, Google Ventures;
Physician, Brigham and Women’s Hospital

Computer Scientist and Medical Student. Were the technology is going?

Messy situation, interaction IT and HC, Boston and Silicon Valley are focusing on Consumers, Google Engineers interested in developing Medical and HC applications — HUGE interest. Application or Wearable – new companies in this space, from Computer Science world to Medicine – Enterprise level – EMR or Consumer level – Wearable — both areas are very active in Silicon Valley

IT stuff in the hospital HARDER that IT in any other environment, great progress in last 5 years, security of data, privacy. Sequencing data cost of big data management with highest security

Constrained data vs non-constrained data

Opportunities for Government cooperation as a Lead needed for standardization of data objects

 

Questions from the Podium:

  • Where is the Truth: do we have all the tools or we don’t for Genomic data usage
  • Question on Interoperability
  • Big Valuable data — vs Big data
  • quality, uniform, large cohort, comprehensive Cancer Centers
  • Volume of data can compensate quality of data
  • Data from Imaging – Quality and interpretation – THREE radiologist will read cancer screening

 

 

 

– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf

 

@HarvardPMConf

#PMConf

@SachsAssociates

@Duke_Medicine

@AstellasUS

@GoogleVentures

@harvardmed

@BrighamWomens

@kyeshwant

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