Artificial Intelligence in Bioscience symposium notes 1 #bioai2017

Writing in haste so not verbatim, accurate, typos etc but fwiw

Improving drug target selection – Philip Sanseau GSK

Cell journal – high definition medicine

IBM Watson now working with Pfizer

Shows typical drug discovery phases 10 yrs 1$bn

Attrition, more likely to fail than succeed in drug discovery

General trends:

GSK trying to change early stages – spend more on target validation

Growth of OMICS data 

30$bn errors in practical usage of analytics? possibly referred to this not sure

ENCORE data

How to pick a target?

GSK in Nature Genetics – gebetics evidence important in success of drug dev

Invested in precompetitive consortium called OPEN TARGET

Academics and BioGen, Sanger collaborators in Europe 

http://www.targetvalidation.org

Use scoring system using evidence strings and ontologies 

Have target quality questionnaires

simple machine learning workflow 

5 features from open targets data eg gene expression, animal models

Chose to include drugs currently in market. Positive or yet to be discovered positive.

Dimensionality reduction reveals structure in the data.

test with 4 classifiers e.g. random forest, neural network, 

Then bagging. 

Model predicts late stage targets more easily than early stage. Generated predictions on 15k genes.

used text mining to validate (Cybyte?) literature list – titles, abstracts. Also looking where it doesn’t overlap.

Issue is volume of data 

when start to subdivide by therapeutic classifiers, so e.g. better for cancer.

Data reproduction and age of data. Can try to reproduce experiments but don’t get same results
Uni of Sheffield Computational Biology, Professor Winston Hide

Likes Eric Schmidt. Works with MIT in gene sequencing consortium. Google / Verily / Alphabet looking for intractable problems they can solve (his words)

Geneticist looking to reduce risk but failures e.g. Merck BACE 1 inhibitors.

Neuroscience/omics datasets are often not big data.

Barbara Engelhardt – tractability, interpretability, reproducibility and validatability. Click sourcing. Mining emails.

Mentions LINCS study, says pathways outperform genes as classifiers.

Heterogenous accuracy – how well data put in system mapped to terms.

Functional pathway fingerprinting – rereleased paper in Bio-Conduct

Functional interaction maps

Learnable maps – context, interpretability. How do genesets relate to other data. Geneset enrichment. 

Algorithm – co-activation matrix of pathways and end up with static co-activation / co-expression network. Can build on. Correkated pathways meaning – work with Mark McCarthy. Have curated list from Alzheimers Society – can see correlated pathways without gene overlap – important – not a model, but promising, can build a map.

Cellular networks – created Biogen platform at Sheffield. Partnered with NIHR acceleration for Alzheimers and another consortium.

GWAS hits and clustering of gene expression. Has topology and can interrogate.

Methodology Rosmap? Consortium – correlated and pathway discorrelated networks. Wants larger corpus of knowledge than asking biologists to validate.

Hubs and superhubs in AD pathways, PDN pathway drug network. 

Centre for Translational Bioinformatics, QMUL, Mike Barnes 

Immune mediated inflammatory diseases IMIDs eg rheumatoid arthritis,  psoriasis affects 2-5% of population. Lots of comorbidities and overlap

But not dealing with static disease e.g. progressive degeneration. Some treatments are effective so don’t have that data i.e. not in clinic. 

Biologic targets – highly connected, if run through pathway mechanism model to show responsive and nin respinsive dusease endotypes. Don’t know why non responsive.

Have 4 projects – use shared infrastructure – PSORT TransSMART & I2B2. Want to go to next stage to compare across diseases e.g.

Brian Tom used LCMM response in RA whilst working on psoriasis data. 

StratMed TranSMART – other classifiers – have clinical classifiers, next step molecule classifiers – hosted on £9million MRC (Medical Research Council) cloud. Persuaded  PIs (principal investigators, mostly clinicians) to share biodata in shared data warehouse. Took to MRC 2 months ago & got funding – IMID BIO UK consortium – going to integrate data from 10k patients in a huge TranSMARTS.

Ai and machine learning nothing compared to cleaning up and accessing health data.

 

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s