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$bn
Attrition, more likely to fail than succeed in drug discovery
GSK trying to change early stages – spend more on target validation
Growth of OMICS data
30$bn errors in practical usage of analytics? possibly referred to this not sure
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
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,
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.