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Posts Tagged ‘biocuration’


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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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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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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