TBI-AMIA 2011 Brain Dump

Here is a very rapid dump of a few of the tidbits that stuck in my brain after spending the last few days at the Translational Bioinformatics (TBI) conference for 2011.

The main recurring themes of interest for the year were:

Social networking for scientists: dirrecttoexperts, vivo, open social, patient recruitment (e.g. army of women).  All efforts are heavily into the linked open data concept.

Temporal reasoning for clinical data: the chronology of the patient's symptoms is an important and underrepresented aspect to mine/model in patient records

Naive Bayes : came up at least twice in important places - once in Lincoln Stein's keynote (used in the automatic expansion of the Reactome database) and once in the nominated best paper by Wei Wei "The Application of Naive Bayes Model Averaging to Predict Alzheimer’s Disease from Genome-Wide Data".  Its an effective method to integrate many many different sources of evidence into a single predictor - a relatively simple machine learning algorithm that scales up well with a lot of data.

Genomic complexity : keynotes about cancer and microbiome highlighted the incredible diversity of genomes.  One example described the difference between a tumor cell and a normal cell one inch away on the order of 50,000 SNP differences - add on top of that genomic rearrangements and epigenetics and well... its complicated.  Complete genome sequencing renders all diseases orphans from a drug development perspective - major changes needed for pharma...  Major opportunities as well.  

Politics : NCBO vs. OBO vs. UMLS

Each of these groups is or will be competing for the same pot of money to solve very similar problems.  While members do collaborate, e.g. key UMLS players advise NCBO, they have serious disagreements about how things should be done and I noticed some pretty emotional discussions - mainly related to fears caused by expected funding cuts.  

name	goal	conflict with OBO	conflict with UMLS
NCBO	empower researchers with access to ontologies and related tools	NCBO is very open about including ontologies.  OBO is not - they believe strongly in their foundry principles (and their distinctive file format) and it annoys them that anyone can get their ontology into NCBO.	See goals.. and think $$ NCBO has a different approach to concept detection that annoys the MetaMap people.  MetaMap feels like NCBO is throwing away years of work on NLP that should be used.
OBO	provide "high quality" collection of biomedical ontologies		Some OBO members seem to have a longstanding dislike of the way that the UMLS is modeled.  OBO believes they are right and the UMLS is wrong and have effectively ignored its presence in the development of their ontologies.
UMLS	empower researchers with access to ontologies and related tools

Unsurprising : lots of people building ontologies to use for structuring data and lots of people applying text mining approaches to mine unstructured data

There was a whole lot more than that, for some more info, have a look at the conference papers, and at Russ Altman's year in review.