text: user734: Are there any examples of researchers data mining articles and papers (e.g., from pubmed or google scholar) in order to derive relations between genes and diseases?  
  
  user73: This existed as a closed silo, at least in 2015. Qiagen has a team of hired students and Post-Docs for collating research papers into their Knowledge Base, an extensive database that is integrated into a few of their commercial products. Qiagen's claim is that by providing a consistently-structured and well-formatted database, the process of research discovery is accelerated. I've got a few notes from a seminar they gave us a couple of years ago:
  
  The curation is better than a free / public database because free databases typically have wrong or inconsistent data
  Their knowledge base is an army of hired MDs and PhDs, 5.3M "findings" as of Nov 05 2015
  Scientific articles have unstructured data, so expert knowledge is required to process the data into a "finding" for their knowledge base
  
  I was already doubtful about their approach, and this doubt was magnified when they started getting into the details of their process. I started to lose trust and write my own thoughts about it:
  
  The knowledge base they have created is private
  The approach is to use a function (i.e. MDs/PhDs) to convert research articles into scientifically-validated associations. However, the function is effectively a black box; Qiagen allow the experts to decide for themselves how a particular article should be converted into a finding.
  The findings in the knowledge base have no strength or likelihood.
  The Experts were encouraged to only enter findings that were "generally accepted by the scientific community. but it seemed like only a single expert was required to get a finding into the knowledge base (i.e. findings weren't curated by other people).
  When using their software (e.g. IPA), p-values of 10^-15 and 10^-22 are reported; these numbers are meaningless.
  The team didn't look at methylation, phosphorylation, etc.; plans are in place to hire additional experts to look at this at a later date.
  
  After listening to their seminar, I realised that Qiagen's idea of a general database that attempts to capture everything isn't going to work well. Experts will disagree about whether a particular association is valid, relevance (or correctness) will differ depending on the area of interest, and the established correctness of a piece of information can change over time.
  It would be possible to create something that might work by adding a lot more metadata to all the "findings" in such a database, but it would need to be a huge, public, collaborative effort. Something on the scale of SRA/ENA, with encouragement from the scientific community to enter details into the database prior to publications being accepted/indexed. If the people who wrote papers entered their findings in such a database, other researchers wouldn't need to be hired to try to understand papers and guess at the findings of published research.
  
  user964: There are many databases that have used publication scraping for oncogenic gene fusions. There are publications for the individual methods they used for their scraping and aggregation.
  
  COSMIC - http://cancer.sanger.ac.uk/cosmic
  TICdb - http://www.unav.es/genetica/TICdb/
  ChimerDB - http://ercsb.ewha.ac.kr/fusiongene/ -
  https://www.ncbi.nlm.nih.gov/pubmed/27899563
  
  
original_text: 734: <p>Are there any examples of researchers data mining articles and papers (e.g., from pubmed or google scholar) in order to derive relations between genes and diseases?  </p>
   73: <p>This existed as a closed silo, at least in 2015. Qiagen has a team of hired students and Post-Docs for collating research papers into their <a href="https://www.qiagenbioinformatics.com/products/qiagen-knowledge-base/" rel="nofollow noreferrer">Knowledge Base</a>, an extensive database that is integrated into a few of their commercial products. Qiagen's claim is that by providing a consistently-structured and well-formatted database, the process of research discovery is accelerated. I've got a few notes from a seminar they gave us a couple of years ago:</p>
  
  <ul>
  <li>The curation is better than a free / public database because free databases typically have wrong or inconsistent data</li>
  <li>Their knowledge base is an army of hired MDs and PhDs, 5.3M "findings" as of Nov 05 2015</li>
  <li>Scientific articles have unstructured data, so expert knowledge is required to process the data into a "finding" for their knowledge base</li>
  </ul>
  
  <p>I was already doubtful about their approach, and this doubt was magnified when they started getting into the details of their process. I started to lose trust and write my own thoughts about it:</p>
  
  <ul>
  <li>The knowledge base they have created is private</li>
  <li>The approach is to use a function (i.e. MDs/PhDs) to convert research articles into scientifically-validated associations. However, the function is effectively a black box; Qiagen allow the experts to decide for themselves how a particular article should be converted into a finding.</li>
  <li>The findings in the knowledge base have no strength or likelihood.</li>
  <li>The Experts were encouraged to only enter findings that were "generally accepted by the scientific community. but it seemed like only a single expert was required to get a finding into the knowledge base (i.e. findings weren't curated by other people).</li>
  <li>When using their software (e.g. IPA), p-values of 10^-15 and 10^-22 are reported; these numbers are meaningless.</li>
  <li>The team didn't look at methylation, phosphorylation, etc.; plans are in place to hire additional experts to look at this at a later date.</li>
  </ul>
  
  <p>After listening to their seminar, I realised that Qiagen's idea of a general database that attempts to capture everything isn't going to work well. Experts will disagree about whether a particular association is valid, relevance (or correctness) will differ depending on the area of interest, and the established correctness of a piece of information can change over time.</p>
  
  <p>It would be possible to create something that might work by adding a lot more metadata to all the "findings" in such a database, but it would need to be a huge, public, collaborative effort. Something on the scale of SRA/ENA, with encouragement from the scientific community to enter details into the database prior to publications being accepted/indexed. If the people who wrote papers entered their findings in such a database, other researchers wouldn't need to be hired to try to understand papers and guess at the findings of published research.</p>
   964: <p>There are many databases that have used publication scraping for oncogenic gene fusions. There are publications for the individual methods they used for their scraping and aggregation.</p>
  
  <ul>
  <li>COSMIC - <a href="http://cancer.sanger.ac.uk/cosmic" rel="nofollow noreferrer">http://cancer.sanger.ac.uk/cosmic</a></li>
  <li>TICdb - <a href="http://www.unav.es/genetica/TICdb/" rel="nofollow noreferrer">http://www.unav.es/genetica/TICdb/</a></li>
  <li>ChimerDB - <a href="http://ercsb.ewha.ac.kr/fusiongene/" rel="nofollow noreferrer">http://ercsb.ewha.ac.kr/fusiongene/</a> -
  <a href="https://www.ncbi.nlm.nih.gov/pubmed/27899563" rel="nofollow noreferrer">https://www.ncbi.nlm.nih.gov/pubmed/27899563</a></li>
  </ul>
  