Building communities of knowledge with Wikidata

As the Wikimedia Movement works to define its strategy for the next fifteen years, it is worthwhile to consider how its recent product Wikidata may fit into that strategy.  As its homepage states,

“Wikidata is a free and open knowledge base that can be read and edited by both humans and machines.” https://www.wikidata.org/

Wikidata is a particular kind of database designed to capture statements about items in the world with references that support those statements.  Because Wikidata is a database, its contents are meant to be viewed in the context of software that retrieve the data through queries and then renders the data to meet the needs of a user in a certain context.  The same data can thus be viewed on Wikidata-specific pages such as https://www.wikidata.org/wiki/Q13561329 and in the infoboxes of Wikipedia articles such as https://en.wikipedia.org/wiki/Reelin.  Importantly, Wikidata content can also be used in applications outside of the Wikimedia family such as http://wikigenomes.org.   

Examples of Wikidata use now include:

• managing interlanguage links across all the Wikipedias
• providing content for hundreds of infobox templates
• answering queries across topics ranging from potential new cancer drugs to today’s birthdays to subway stations in Paris.  

The molecular biology community (and in particular the Gene Wiki group) has embraced Wikidata as a global platform for knowledge integration and distribution.  To help envision how Wikidata may fit into the strategic vision of the WMF movement, it is worth taking a look at how and why this particular community is using Wikidata.  

History of the Gene Wiki initiative

The sequencing of the human genome at the beginning of this century and the consequent rush of data and new technology for producing even more data fundamentally changed how research in biology is conducted.  Before the year 2000, research typically proceeded with a single gene focus.  A typical PhD thesis would entail the analysis of the genetics or function of one gene or protein at a time.  A few years after the first genome however, it became possible to measure the activity of ten’s of thousands of genes at once resulting in an omnipresent problem of generating interpretations of experimental results containing hundreds of genes.  While a scientist may come to grasp the literature surrounding a single gene quite well, it is not possible to know everything there is to know about all 20,000+ genes in the genome - particularly when this knowledge is expanding on a minute by minute basis.  As a consequence, there arose a need to produce summaries of what was known about each gene so that researchers could quickly grasp its nature and easily find links to more detailed references as needed.  By 2008, many different research groups published wikis attempting to allow the scientific community to generate the required articles, e.g. WikiProteins, WikiGenes, and the Gene Wiki.  The Gene Wiki project was unique among this group as it anchored itself directly to Wikipedia and, likely as a result of that decision, has enjoyed long term success.  This initiative works within the English Wikipedia community to encourage and support the collection of articles about human genes.  Its main contributions are the infobox seen on the right hand side of of these articles and software for generating new article stubs using that template.  

Wikidata and the Gene Wiki project

For the past several years, the Gene Wiki core team (funded by an NIH grant) has focused primarily on seeding Wikidata with biomedical knowledge.  In comparison to managing data via direct inclusion and parsing of infobox templates as before, this makes the data much easier to maintain automatically and, importantly, opens it up for use by other applications.  As a result, Wikipedia isn’t the only application that can use this structured information.   One of the first products of that process was a new module (Infobox_gene) that draws all the needed data to render the gene infobox dynamically from Wikidata, greatly reducing the technical challenge of keeping the data presented there in sync with primary sources.  

In addition to the relatively simple collection of gene identifiers and links off to key public databases that are presented in the infoboxes, Wikidata now has an extensive and growing network of knowledge linking genes to proteins, proteins to drugs, drugs to diseases, diseases to pathogens, pathogens to places, places to events, events to people, and so on and so on.  This unique, open, referenced, knowledge graph may eventually become the closest thing to ‘the sum of all human knowledge’.  Capturing knowledge in this structured form makes it possible to use it in all kinds of applications, each with their own community-specific user experiences.  As a case in point, the Gene Wiki group created Wikigenomes based primarily on data loaded into Wikidata.  This was followed quickly by Chlambase, an application specifically focused on distributing and collecting knowledge about different Chlamydia genomes.  These applications provide domain-specific user interface components such as genome browsers that are needed to present the relevant information effectively and thereby attract the attention of specialist users.  These users, in turn, have the opportunity to contribute their knowledge back to the broader community through contributions to Wikidata that can be mediated by the same software.  

Wikidata and the world

The molecular biology research community, as represented by the Gene Wiki project, are early adopters of Wikidata as a community platform for the collaborative curation and distribution of structured knowledge, but they are not alone.  The same fundamental patterns are already being applied by other communities, e.g. those interested in digital preservation and open bibliography.  In each case, we see communities working to transition from the current dominant paradigm of private knowledge management towards the knowledge commons approach made possible by wikidata.  This is not unlike the transition from the world of the Encyclopedia Britannica to the world of Wikipedia.  The only important difference is that the knowledge in question is structured in a way that makes it easier to reuse in different ways and in different applications.  

Wikidata provides a mechanism for massively increasing the global good generated by the Wikimedia Foundation’s work by capturing knowledge in a form that can be agilely used to empower all manner of software with the sum of human knowledge.  

Poof it works - using wikidata to build Wikipedia articles about genes

Infobox for ARF6,
rendered entirely from
content Wikidata

The Gene Wiki team has been hard at work filling wikidata with useful content about genes, diseases, and drugs using the new and improved ProteinBoxBot.  Now, we are starting to see the fruits of this labor in the context of Wikipedia.

The Gene Wiki project has programmatically created and maintained the infoboxes to the right of all the articles in Wikipedia about human genes since about 2008 [Huss 2008].  This process has entailed the construction of a unique template containing all of the relevant data for each gene.  For example, here is the code for the template for the ARF6 gene.  As Wikipedia previously had no database, that is where the data was stored.  Altering that content programmatically involves parsing that template as a string.  Its ugly (sorry Jon) and there are more than 11,000 of these templates to maintain (one per gene in Wikipedia).

Now, the same data can be represented in Wikidata, a queriable, open graph of claims about the world backed by references and specified by qualifiers [Vrandečić 2014].  Now that the content needed to render the infobox is all there, we can convert 11,000+ complex templates that require string parsing to maintain to a single, re-usable template for all of them.

The first cut at the new template is {{infobox gene}}.  If you put that on any article about a human gene, you ought to get the complete infobox for the article without any further ado.  Poof!  You can view it in action on this revision for ARF6.  We haven't rolled out the new template across all the articles yet, but hope to see that happen in the coming months.  Remaining issues include: better error-handling in the template code, better ways to give users the ability to edit the associated data in wikidata, and updates to all of the code that produces gene wiki articles.  If you want to help, chime in on the module:wikidata thread.

Local talks on games and Gene Wiki

Last Friday I had the honor of speaking at the Salk Institute 'Systems to Synthesis' Symposium.  I introduced the idea of games with a biological purpose, showed off our early results with Dizeez and plugged some of the prototypes appearing at genegames.org.  The slides for the presentation are up on slideshare.

Tomorrow, Erik Clarke from our group will be speaking about his work on the GeneWiki+ at the Semantic Media Wiki conference up in Carlsbad.

Gene Wiki SPARQL endpoint

Thanks to Leyla and Alex Garcia-Castro from UniProt and Florida State University respectively, we now have access to a SPARQL endpoint for the data in the Gene Wiki.  Access it live here:
http://virtuoso.idiginfo.org/sparql
(update on 4-28-12 , that is down and a live one is currently available at
http://199.102.237.69:8890/sparql
)

Here is one query that you might like to try that finds gene-disease links that we have mined from the text:

PREFIX wiki: <http://genewikiplus.org/wiki/Special:URIResolver/>

PREFIX property: <http://genewikiplus.org/wiki/Special:URIResolver/Property-3A> 

SELECT ?gene ?disease ?gene_name ?disease_name ?doid
WHERE {
 ?gene property:Is_associated_with_disease ?disease .
 ?gene property:HasSNP ?snp .
 ?snp property:Is_associated_with_disease ?disease .
 ?gene rdfs:label ?gene_name .
 ?disease rdfs:label ?disease_name .
 ?disease rdf:type ?disease_cat .
 ?disease_cat property:HasDOID ?doid .
 ?gene rdf:type wiki:Category-3AHuman_proteins .
}

How it works in brief

1. Articles from the Gene Wiki and from SNPedia are transferred to genewikiplus.org
2. As they go in, they are converted into a semi-structured form that enables queries in semantic media wiki.
3. We dump the entire thing out as one giant RDF file.
4. Leyla loads the RDF into their Virtuoso server (and performs some enhancements such as linking directly to UniProt RDF).
5. and wa la!

(More details about the generation of the genewiki+ are available in this soon-to-be-published paper about the SNPedia mashup and this paper about Semantic Wiki Links in Wikipedia.)

Cool next steps

The RDF has OWL:sameAs links between all the Gene Wiki entries and their RDF equivalents in DBpedia and in UniProt's RDF representation.  It should be possible to explore connections that span these three (four including SNPedia) resources using Linked Data technologies like Virtuoso's Sponger.

Go forth! Play with our data!

Semantic Wiki Links article published

A while back, I posted an entry here called "Stepping towards a Semantic Wikipedia" that quickly became the most-visited post on this blog.  Today, after several months of waiting, I discovered by accident that the official peer-reviewed article on the same subject is now available (the editors never bothered to tell us that it came out).  The article elaborates on a pattern that allows Wikipedia users to write content directly into the semantic Web.  The article is called "Building a biomedical semantic network in Wikipedia with Semantic Wiki Links" and is now available at DATABASE: The Journal of Biological Databases and Curation.  Here is the abstract:

"Wikipedia is increasingly used as a platform for collaborative data curation, but its current technical implementation has significant limitations that hinder its use in biocuration applications. Specifically, while editors can easily link between two articles in Wikipedia to indicate a relationship, there is no way to indicate the nature of that relationship in a way that is computationally accessible to the system or to external developers. For example, in addition to noting a relationship between a gene and a disease, it would be useful to differentiate the cases where genetic mutation or altered expression causes the disease. Here, we introduce a straightforward method that allows Wikipedia editors to embed computable semantic relations directly in the context of current Wikipedia articles. In addition, we demonstrate two novel applications enabled by the presence of these new relationships. The first is a dynamically generated information box that can be rendered on all semantically enhanced Wikipedia articles. The second is a prototype gene annotation system that draws its content from the gene-centric articles on Wikipedia and exposes the new semantic relationships to enable previously impossible, user-defined queries."

I will also be presenting a poster about this and related work at the upcoming Biocuration meeting in Washington DC.  Stop by for a chat!

Interview with Results for Development Institute

A few days ago, Hassan Masum of the Results for Development Institute, asked me some questions about the Gene Wiki as a platform for collaboration and discovery in the health sciences.  The results of the interview were posted today on their blog as the first entry in what will be an ongoing series of interviews with people in the business of collaborative innovation.  It was fun talking with Hassan and I'm looking forward to seeing the rest of the interviews as they come out.

Results for Development looks like a really interesting kind of non-profit.  One of the many things that they do is to take really bright, creative people, like Hassan, and task them with identifying key avenues to accomplish major transformational change.  Their mission statement is:

"R4D's mission is to spark innovative ideas and catalyze high impact actions that reduce poverty and improve lives in developing countries."

Its very exciting to know that the Gene Wiki might play some very minor role in realizing a vision like that.

Gene Wiki on NAR database cover

The Gene Wiki Rainbow just got a little bit more famous.  Check it out on the cover of the 2012 Nucleic Acids Database issue.  Thanks again Martin K.!

The Gene Wiki on the cover of NAR

Mining the Gene Wiki

Our article about mining ontology-based gene annotations from the text of the Gene Wiki just came out at BMC Genomics.  Yay!

In the article, we discuss the results of what I think might be the simplest text-mining strategy that could possibly work.  Based on the premise that each Gene Wiki article is fundamentally about one particular gene, we make the simplifying assumption that all of the concepts detectable in the article are descriptors of what that gene does.  With those assumptions in place, we use the NCBO annotator to detect concepts from the Gene Ontology (GO) and the Human Disease Ontology (DO) in the text of articles about genes.  Each detected occurrence thus produces a candidate annotation for the gene.  From the article:

For example, we identified the GO term ‘embryonic development (GO:0009790)’ in the text of the article on the DAX1 gene: “DAX1 controls the activity of certain genes in the cells that form these tissues during embryonic development”.  From this occurrence, our system proposed the structured annotation ‘DAX1 participates in the biological process of embryonic development’.  Following the same pattern, we found a potential annotation to the DO term ‘Congenital Adrenal Hypoplasia’ (DOID:10492) in the sentence: “Mutations in this gene result in both X-linked congenital adrenal hypoplasia and hypogonadotropic
hypogonadism”.

We found that, in terms of precision, this simple approach worked pretty well on detecting gene-disease  annotations (90-93%) but not nearly as well at detecting gene-function (GO) annotations (48-64%).  As you might expect, the recall equation worked in the opposite direction with many more potential GO annotations discovered (11,022) then DO annotations (2,983).  Though there was some overlap, the majority of the predicted annotations did not have any match in existing annotation databases, showing that the Gene Wiki contains some knowledge that centralized resources like the Gene Ontology Annotation database do not yet represent and that basic text mining provides a way to access that knowledge computationally.

But, you say, that precision for the GO is really low, what use is this really?  For applications that require 100% accuracy, like a curated database, well you would need to curate the predicted results and that might be quite a lot faster than searching through PubMed to find them all from scratch.  As it turns out, there are also other kinds of applications that can take advantage of data like this that has noise in it.  As long as there is a strong signal within the noise, probabilistic techniques, like enrichment analysis, can work.  This is possible because, although many of the individual annotations might turn out to be incorrect, as a group they are far far from random.

For more details, read the paper ;).

Gene Wiki article out today at NAR

The articles for the annual database issue are starting to appear in the NAR collection.  My favorite one this year is, immodestly perhaps, ours about the Gene Wiki!  The simple message here is that the Gene Wiki is continuing to grow and that the content remains very high quality overall.  For more information, the abstract is below, and of course the paper is freely accessible online:

"The Gene Wiki is an open-access and openly editable collection of Wikipedia articles about human genes. Initiated in 2008, it has grown to include articles about more than 10 000 genes that, collectively, contain more than 1.4 million words of gene-centric text with extensive citations back to the primary scientific literature. This growing body of useful, gene-centric content is the result of the work of thousands of individuals throughout the scientific community. Here, we describe recent improvements to the automated system that keeps the structured data presented on Gene Wiki articles in sync with the data from trusted primary databases. We also describe the expanding contents, editors and users of the Gene Wiki. Finally, we introduce a new automated system, called WikiTrust, which can effectively compute the quality of Wikipedia articles, including Gene Wiki articles, at the word level. All articles in the Gene Wiki can be freely accessed and edited at Wikipedia, and additional links and information can be found at the project's Wikipedia portal page:http://en.wikipedia.org/wiki/Portal:Gene_Wiki."

Stepping towards a Semantic Wikipedia

(Update, check out our publication in Database for a full-length, peer-reviewed version of this article.)

It is now possible to specify the nature of the relationships between things described by Wikipedia articles directly in the context of the article.  The image below is a screenshot taken a few moments ago of the Phospholamban article on Wikipedia (with excited arrows added).  The infobox at the top right is dynamically generated from semantic markup in the article using a Wikipedia user script written by my colleague Sal and accessible from his user page. 

Semantic markup now live in Wikipedia

How it works

Wikilinks in the article have been annotated with the kind of relationship that they indicate using the Semantic Wiki Link (SWL) template. The template allows any Wikipedia editor (including you!) to specify the type of connection that exists between the article where the link is being placed and the target of the link. This information is encoded following the microformat pattern. Essentially, we encode the meaning of the links in class attributes that wrap the link.

This works as follows:

1. Editor inserts a SWL into a Wikipedia article with this syntax:
• {{SWL | target=protein kinase S | label=PKA | type =substrate_for}}
• This means "the concept where you see this link is related to protein kinase A (labeled PKA) with the relationship type "substrate for". So, in the example above, it says: "Phospholamban is a substrate for PKA".
• The {{}} denotes a Wikipedia template. Templates can take parameters (here parameters are separated by |'s) and use them to produce new WikiText dynamically which, in turn, is rendered as HTML when a page is loaded.
2. When the page is rendered, the template generates the following semi-semantic HTML markup (with some formatting omitted for clarity):

• 
  
3. Programs, like the script that generated that infobox and added the green highlighting, can look for the SWL class attribute can then extract the meaning of the SWL links based on the class of its first child element - here "substrate_for".
4. In addition, when the template is processed it adds a category to the article it is placed on that corresponds to the relationship type. (See for example, the category for substrate.) This category provides a logical grouping (e.g. all things that serve as a biochemical substrate) but, perhaps more importantly, it provides a place to record the meaning of the relationship. This meaning can be defined as text, but can also be defined through reference to external sources such as ontologies on the semantic web.

Why its awesome

This pattern makes it possible for the vast number of Wikipedia users to simply and easily contribute machine readable content to the Web. This enormous user community collaboratively created the world's largest encyclopedia and one of the most valuable websites on the planet. Who better to help build the semantic Web? While, technically, the microformat-like implementation leaves much to be desired in terms of its robustness and its precision, it is a solution that can work. This is demonstrated by the success of projects like Google's recent recipe search that are based entirely on simple microformats.

How you can help

This is a new idea that not everyone in Wikipedia will be thrilled to see. They will claim that the SWLs will clutter the markup and will not provide enough value to make it worth it because Wikipedia itself does not support semantic links. You can help by:

• Using the template to enhance articles.
• Writing code that makes use of the added meaning such as user scripts, aggregators, or scripts that import the relationships into other structured repositories like FreeBase or DBpedia.
• Helping define the nature of the semantic links (at their associated category pages) and mapping them to properties defined in ontologies.
• Discussing (and voting for) the idea on the various 'talk pages' on Wikipedia.

Why its awesome again

Did I mention that this pattern makes it possible for the vast number of Wikipedia users to simply and easily contribute machine readable content to the Web? Thats pretty cool if you think about it...

Update: To make the user script work for you so you can see the infobox, do this:

• Create a Wikipedia user account if you don't have one already
• Go to/create your user page. (e.g. my user name there is i9606 and my user page is located at http://en.wikipedia.org/wiki/User:I9606 )
• Edit your user page add this to it - 

[[/common.js]]

• Visit your new common.js add this to it - 

importScript('User:Sal9000/SWLinfobox.js');

• When that is saved, you should be all set. Now go visit an enhanced page like Phospholamban and look for the green box at the upper right corner.

The script will run whenever you access a Wikipedia page while you are logged in to your account. Its a lot like GreaseMonkey, (which I've had some fun with in the past) but its not tied to your browser and will only work on Wikipedia. If enough people like a user script, it can be added to the default set of Wikipedia user preferences.. which would be pretty cool ;). The best part? You (or a programmer friend of yours) can write your own script and make it do whatever you want with the data!

GenomeWeb article on wiki mining mashup

I guess we had a reporter in the audience at our talks at ISMB.  Its always fun to get noticed though its a little scary to see something written about your work without ever having spoken with the author.  In this case they got almost everything right.  Thanks for the article Genome Web!

Gene Wiki Pulse tweets again!

After a month long summer vacation, the Gene Wiki Pulse Twitter feed is once again alive and tweeting.  While I suppose this little guy probably isn't going to get anyone tenure, its one of my favorite pet projects here at Gene Wiki Central and I'm very happy to have resurrected it.  I love it because it provides a live, global view of the work being conducted on the Gene Wiki by thousands of people all around the world.  My interest is largely in understanding and improving the knowledge assembly system running here as a whole, so the 'oooh look how much is happening! feeling' is enough for me to be an excited follower but I do hope that the updates are also useful to people that simply want to know more about their favorite genes...

For the technically inclined, the vacation was the result of the system clock on our server drifting forward about 10 minutes into the future (and ISMB and the move to the new office).  Since the pulse only looks two minutes into the past, it suddenly became highly unlikely to pick up any new edits!  Thanks to our new associate Erik for fixing the network time on our server and for suggesting the new, much more efficient watchlist-driven approach to monitoring Gene Wiki activity that is running now.  If you want to write your own Wikipedia watching and tweeting program you can find the source code for this in the gene wiki repository in the class StreamAccess.

Gene Wiki Rainbow

Last summer I posted an image of the Gene Wiki hyperlink network, aptly titled "the gene wiki hairball".  The image was picked up by noted artist/scientist Martin Krzywinski and used as an example of why hairballs are a terrible visualization.  Perhaps out of guilt for making an example out of us and/or perhaps out of interest he has helped us improve our thinking about how to visualize networks substantially.  Here is a Circos view of the top 100 genes in the gene wiki, the editors that created the articles, and the diseases and compounds that the genes are linked to.  It will be presented as a guerilla poster[1] at ISMB this year so please stop by and have a closer look!

Aside from bringing an artistic aesthetic to scientific illustrations, one of Martin's main contributions (IHOP) is that he understands and uses space to convey meaning effectively.  In a hairball, the only consideration of space in the layout algorithm is to reveal as many nodes as possible.  In a Circos diagram, place can be attached to semantics.  This fundamental idea is used to an even greater extent in Martin's latest layout invention, Hive Plots.  Watch out for the Gene Wiki Hive Mind visualization - or better yet, write to us and help us build it!

[1] guer·ril·la post·er /gəˈrilə/ /ˈpōstər/

Noun: An uninvited poster displayed at a scientific conference.

Integrating the Gene Wiki with traditional publishing?

While we (Andrew Su and I) like to talk about the successes of the Gene Wiki - articles like the one for Reelin that represent arguably the best consolidated body of text associated with the gene - there remain some rather glaring holes in its content.  A couple months ago I had a look for under-developed articles linked to genes with extensive numbers of publications.  With a small bit of hacking I uncovered a list of 2,553 genes that were linked directly to more than 20 PubMed citations (using NCBI's gene2pubmed) but had less than 100 words of text in their Gene Wiki article. (Up to the previous period, this post contained 105 words.)  From this list I found 151 genes with more than 100 PubMed citations and less than 100 words of wiki text.

An example is the PIN1 gene.  When the analysis was run, this gene was linked to 154 citations in PubMed yet had only 2 sentences in the Gene Wiki.  So...  how do we fill in these gaps?  This is, of course, the fundamental question associated with wikis or any other attempt to harness community intelligence and there is no easy answer.  One model that we are very interested in was pioneered by Alex Bateman and colleagues at the journal of RNA Biology.  When hopeful authors submit an article about a new RNA family to the journal, it is a condition of publication that they contribute an article to Wikipedia about that family.   Aside from being a generally good thing to do as far as sharing knowledge with the world, these articles are subsequently used to manage the annotations for RNA families in the Rfam database (e.g. snoZ107_R87).  After a few years of operation, the Rfam team published an article that, among others things, celebrated the success of the Wikipedia connection.  So, how might we expand upon this model to tackle the challenges facing the Gene Wiki?

The beauty of this approach is that it does not rely on any changes to the incentive system currently operational in science.  Scientists need to publish in peer-reviewed journals.  Rather than complaining about the inefficiency of this outdated process and suggesting social changes with no obvious way to achieve them, lets see what we can do to make the system work for us as it stands.  Lets create a way for scientists to obtain real publications in real journals and have Gene Wiki article content generated as a natural part of the process.   Here is one idea.

A Gene Wiki Meta-Journal Special Edition

In this model we would work with a number of smallish, topic-focused journals to requisition short review articles about the molecular function and phenotypic relevance of individual human genes.  (By phenotypic relevance I mean the connection between the gene and something that non-scientists might care about such as a role in a disease or a connection to some human attribute such as height, hair color or athletic performance.)  These review articles would be published in journals appropriately matched to the key phenotypes associated with the gene.  For example, we might imagine requesting a review article in the Journal of Investigative Dermatology about the gene Filaggrin because the most important variations in this gene have been shown to relate to skin conditions such as eczema.  Each of these phenotypically targeted gene review articles would be linked from and would link back to a central article that described the meta-journal concept - ideally in a journal with an audience broad enough to span each of the more niche-specific journals that participated in the experiment.  Following the RNA Biology model, a condition of publication would be to update or create the relevant Gene Wiki article with content from the submitted review article.  

While logistically challenging, this approach appeals to me because it continues with the theme of tapping into the 'Long Tail'.  If we can distribute the labor out among a larger number of journals we ought to be able to connect with a larger number of individual contributors.  In addition, it might be appealing to the editors and contributors to more niche-specific journals to participate in a project with broad visibility.   As an alternative, we might consider attempting to organize a gene-focused special edition similar to the annual Nucleic Acids Research database edition in one gene-focused journal (e.g. Genome Biology), but it seems unlikely that this approach would have the same potential breadth of impact.  Also, following a phenotypic rather than molecular orientation aligns well with Wikipedia's notability criterion and hence might help to generate article content that would meet with less resistance from current Wikipedia editors.

If you have any thoughts on this idea (or if you have better ideas!) I would love to hear from you.

Gene Wiki on Twitter

As you may notice in the new live twitter feed to the right (if you are reading this at blogger), a new entity has entered the twittersphere.  The GeneWikiPulse provides a live (well within a couple minutes) feed of editing activity on the Gene Wiki.  In a nutshell, a program is watching all the articles in the Gene Wiki and doing its level best to turn every editing event into an informative message in 140 characters or less.  Each such tweet is signed with a hashtag for the official gene symbol and the responsible editor.  

This is the first little step we've taken to increase community engagement for Gene Wiki since I got started and I'm very curious to see how people react.  If you have any ideas for improving this stream, let me know!

For a tad more info., check out the post describing the Gene Wiki Pulse on the BioGPS blog.

Oh yes, and please go ahead and follow the GeneWikiPulse!

NCBO Annotator versus MetaMap on GO concept detection

For the past little while I've been working on extracting candidate Gene Ontology annotations from the hypertext of articles in the Gene Wiki.  In this work I have been using two of the premier tools for concept recognition in bioinformatics - MetaMap from the National Library of Medicine and the Annotator from the National Center for Biomedical Ontology.  As elaborated on in this article, these systems work very differently and, depending on the input text, can yield substantially different results.  Since I was at a loss when I first had to decide which tool to use, I thought I'd share some of the results of my experiments in the hopes that I might help some one else along in their decisions.

Input: text from about 10,000 Gene Wiki articles (both complete sentences and the titles of pages linked to from a gene page)
Output: concepts from the GO with some form of linguistic match to the text.

Current Rounded Results:
GO concepts detected: Annotator about 20,000, MetaMap about 35,000, Intersection about 14,000 (See diagram below)

In this case MetaMap is the clear winner in terms of recall.  Based on some casual manual inspections and some less casual comparisons to other GO annotation sources, the Annotator earns a very slight edge in terms of precision (it produces slightly fewer false positives).  For my application, I'm pooling the results of both tools.

Another important consideration is speed of execution.  For my experiments, a locally installed version of MetaMap took about twice the time to run the same jobs as the Annotator Web service - despite the lag from network latency for the Annotator.  The output from both tools was fairly easy to parse.  (I found it much easier to simply parse the results myself than to work in the context of UIMA wrappers which are available for both systems.)

So there you go, same same but different.

Presenting at San Diego Semantic Web Meetup

In a few weeks, I will be giving a seminar for the San Diego Semantic Web Meetup group. Its going to cover a fair amount of the same material we covered at the NCBO webinar, but you should come anyway because a) I have new data and b) I'm going to be there live (so you can bring your rotten tomatoes!).

** Update, I am now excited to be presenting with Denny Vandrecic, an expert on ontology evaluation and one of the founders of the Semantic Media Wiki.

Gene Wiki talk saved at NCBO

A webex recording of our webinar "The Gene Wiki: Cultivating and mining community intelligence in biology" is now available on the NCBO website. It was a little weird talking to myself - in particular it was totally impossible to know if any attempt at humor was successful.. Overall I think it went pretty well though since we had a good number of relevant questions from the audience. I imagine it would probably be a good idea from a learning and improving perspective but I have yet to bring myself to actually listen to myself speak.. Thankfully the webcam was not in effect so I'll never have to watch myself. (Andrew had the first half and I had the second.)

Gene Wiki Jamboree 1 - post mortem

Last week I had the honor of leading a Gene Wiki Jamboree at the annual meeting of the FaceBase Consortium (an association of researchers focused on understanding the genetics craniofacial development). The FaceBase group is creating a centralized resource for sharing their data and their knowledge with the hope of generally cross-fertilizing ideas and specifically enabling analyses that span the domains and datatypes of the very diverse members in the group. As part of this effort they want to compile and share their knowledge about genes that are relevant to craniofacial development. Rather than, or perhaps in addition to, the creation of their own wiki they are hoping to do this in the context of the Gene Wiki - hence my appearance at their meeting.

In general, the goal of a 'jamboree' in the context of biology is to bring a group of people together to rapidly assemble their knowledge on some specific subject. To my knowledge, the first usage of the term in that fashion was the Drosophila Genome Annotation Jamboree at Celera in 1999 (please let me know if there was an earlier use). In that case, more than 40 scientists gathered together for 2 weeks to define the boundaries of the fly genes and to classify their function. Not being a company like Celera nor having a whole, brand new genome to work on, the size of our group and our time allotment was much smaller. In total we only had about 8 people and 90 minutes...

Given those constraints, this jamboree was really more of a tutorial. It took about 45 minutes to get everyone in the room set up with an account and through their first 'edit this page' experience. So in the end, not a lot of content was generated. But, everyone did succeed in making an edit, seemed happy with their experience, and were clearly much more likely to edit something in the future than they were before the jamboree. So.. I think it was a success given the situation.

A couple notes on the process:

1. We set up a friendfeed group so that people could ask questions remotely and could track what others were doing (we added RSS feeds for the contributors edits). This was not used at all. None of the participants was a previous friendfeed user and they were also completely new to the Wikipedia experience. It seemed that the attention load of learning the Wiki way was more than enough to occupy them. Also, everyone was on laptops which limited the screen space for a flow-interface like friendfeed. I still like the idea of the ff-group as a live view on the progress and thoughts of the community, but for an in-person jamboree we will have to think of a different approach to achieve that kind of collective awareness. Perhaps a separate projector with the flow view..
   
2. Principle Investigators with their own very busy agendas really aren't the best participants in a jamboree. To be more effective, we need people with more time to focus on the task (graduate students please..)
3. We need more time! Doing a good job editing the text of a scientific review article isn't the easiest thing to do, more time would really help. This is especially the case when working in an editing environment where, if you are adding references, the text you are typing starts looking like this:
   

VEGFR-2 appears to mediate almost all of the known cellular responses to VEGF {{cite journal | author = Holmes K, Roberts OL, Thomas AM, Cross MJ. | title = Vascular endothelial growth factor receptor-2: structure, function, intracellular signalling and therapeutic inhibition. | journal = Cell Signal. | volume = 19 | issue = 10 | pages = 2003–2012 | year = 2007 | month = Oct | pmid = 17658244}}.

Yuck...

4. Watch out for edit conflicts. When some one is editing their very first page, the last thing that you want to happen is for some one else to be editing it at the same time. While it could be construed as a useful lesson, its really more than is needed for edit #1. This is something that anyone leading a tutorial like this should consciously avoid since its easy to accidentally create situations where it is likely - for example, by going to a sandbox page or other and having everyone edit it at the same time or even just posting a list of pages to edit (most people will start on the top).
5. Finally, it is vital to have some kind of central coordinating page where links to help resources and to-do lists can be assembled. I probably didn't give this enough thought on this run, but will be sure to make this a priority in my preparations if I get the chance to lead another Jamboree.

Gene Wiki Hairball 1

Here is a view of the hyperlink network in the Gene Wiki.

Generated as follows:

1. Query Wikipedia for articles in the Protein category (using the API with some help from the Bliki java client library).
2. Extract links to other Wikipedia articles
3. Build link network in Cytoscape (organic layout)

Pretty I think, now what do we do with it?