<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>

CC BY-SA 3.0
2017-07-09T22:32:46.213Z
2017-07-09T22:32:46.213Z
Bioathlete
<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>

CC BY-SA 3.0
2017-07-10T11:42:21.230Z
Thomson-Reuters is doing a similar thing, but I agree that "an association" is a complex thing and perhaps not to be taken at face value. Protein A may interact with protein B under lab conditions but whether this is a generally correct and useful thing is unclear.
agapow
CC BY-SA 3.0
2017-07-09T22:50:16.190Z
2017-07-09T22:50:16.190Z
gringer
<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>

CC BY-SA 3.0
2017-07-09T21:39:08.840Z
Do you have a specific question about text mining?
jaslibra
CC BY-SA 3.0
2017-07-09T21:43:56.213Z
@jaslibra, it's a general question about this approach. For example to decide if a gene is tumor suppressor one. What kind of mutation are associated with pan-cancer, predict if a gene is oncogene. Basically, it has endless potential
0x90
CC BY-SA 3.0
2017-07-09T23:00:09.183Z
This is really way too broad to be a good fit for this site. Unless you'd be satisfied with a yes, this can only be answered with a list fo examples. The simple truth is that *everyone* reads articles to learn the state of the art and then might move on to the lab to validate whatever inference they made. Please try and [edit] to make it more specific.
terdon
CC BY-SA 3.0
2017-07-09T23:01:09.903Z
@terdon the question is whether a person reads it or a machine. That's the whole point.
0x90
CC BY-SA 3.0
2017-07-09T23:05:10.623Z
The short answer is yes.  You could scrape pubmed for all the papers that are based on scraping data.  ;)
Bioathlete
CC BY-SA 3.0
2017-07-09T23:09:05.630Z
@0x90 both. Depends on what you need to do, that's why it's too broad. And you are not actually asking that, by the way. You are asking: *Are there any examples of researchers data mining articles and papers [. . . ] in order to derive relations between genes and diseases?* If you want to know how data mining works, that would be a very different question. If you want to know whether it is a machine or a human, you would have to give a specific example since both do it in different cases.
terdon
CC BY-SA 3.0
2017-07-10T04:56:51.730Z
I'll provide an answer here, since the question got locked for whatever StackOverflow-ish reason. You might be interested in SmartFigures: http://smartfigures.net/ which uses data in figures to annotate an ontological database of genes, diseases, etc. See: https://vimeo.com/158555231 and https://vimeo.com/170011946 for more detail. Hope this helps and welcome to Stack Overflow!
Alex Reynolds
CC BY-SA 3.0
2017-07-10T08:46:32.993Z
If you feel this question was put on hold unfairly, please open a meta discussion. I am open to being corrected. I still feel this is very broad if you don't edit to limit to specific examples. I mean, of course researchers get disease associations from papers! Where else would they come from?
terdon
CC BY-SA 3.0
2017-07-10T09:14:04.630Z
Also, the best way for you to reopen this question is to edit it and make it ask something specific instead of a vague "are there any examples". Explain exactly what you are thinking of (you made some more specific points in your comments, for example) do some research into what text mining is and how it can be applied and ask something specific. Or, you could change your question from "Are there any examples" to "*how* can this be done" which would also make it answerable.
terdon
<gene><genome><cancer><data-mining>
Using data mining of papers in order to derive genomic connections
# Using data mining of papers in order to derive genomic connections

(Asked by: username_0 on 2017-07-09)

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?

**Tags:** gene, genome, cancer, data-mining
> **Comments:**
> **username_1**: Do you have a specific question about text mining?  
> **username_0**: @jaslibra, it's a general question about this approach. For example to decide if a gene is tumor suppressor one. What kind of mutation are associated with pan-cancer, predict if a gene is oncogene. Basically, it has endless potential  
> **username_2**: This is really way too broad to be a good fit for this site. Unless you'd be satisfied with a yes, this can only be answered with a list fo examples. The simple truth is that *everyone* reads articles to learn the state of the art and then might move on to the lab to validate whatever inference they made. Please try and [edit] to make it more specific.  
> **username_0**: @terdon the question is whether a person reads it or a machine. That's the whole point.  
> **username_3**: The short answer is yes.  You could scrape pubmed for all the papers that are based on scraping data.  ;)  
> **username_2**: @0x90 both. Depends on what you need to do, that's why it's too broad. And you are not actually asking that, by the way. You are asking: *Are there any examples of researchers data mining articles and papers [. . . ] in order to derive relations between genes and diseases?* If you want to know how data mining works, that would be a very different question. If you want to know whether it is a machine or a human, you would have to give a specific example since both do it in different cases.  
> **username_4**: I'll provide an answer here, since the question got locked for whatever StackOverflow-ish reason. You might be interested in SmartFigures: http://smartfigures.net/ which uses data in figures to annotate an ontological database of genes, diseases, etc. See: https://vimeo.com/158555231 and https://vimeo.com/170011946 for more detail. Hope this helps and welcome to Stack Overflow!  
> **username_2**: If you feel this question was put on hold unfairly, please open a meta discussion. I am open to being corrected. I still feel this is very broad if you don't edit to limit to specific examples. I mean, of course researchers get disease associations from papers! Where else would they come from?  
> **username_2**: Also, the best way for you to reopen this question is to edit it and make it ask something specific instead of a vague "are there any examples". Explain exactly what you are thinking of (you made some more specific points in your comments, for example) do some research into what text mining is and how it can be applied and ask something specific. Or, you could change your question from "Are there any examples" to "*how* can this be done" which would also make it answerable.  

---

## 2 answers

### Answer by username_3 on 2017-07-09. Score: 4

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>


---

### Answer by username_5 on 2017-07-09. Score: 3

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](https://www.qiagenbioinformatics.com/products/qiagen-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.

> **Comments:**
> **username_6**: Thomson-Reuters is doing a similar thing, but I agree that "an association" is a complex thing and perhaps not to be taken at face value. Protein A may interact with protein B under lab conditions but whether this is a generally correct and useful thing is unclear.  

---

