#11thICCS Natalia Aniceto on Gearing transcriptomics towards HTS: Cmpd shortlisting from gene expression using in silico information
Gene expression provides additional insight beyond phenotype into biological processes, which can be used to find new drugs. Original paradigm: focussing on single target. But current paradigm is to use systems biology to look at a set of genes.
Treatment -> transcvriptomics signature -> phenotypic readout. The signature gives additional insight that may be useful to treat the disease.
What data is available. Library of integerated cellular signatures (LINCS). from Broad Institute. Allows systematically mapping gene to mode of action to disease. Difficult to query it except via exptal data. Not suitable for HTS.
Try to find a tool to select cmpds that elicit a desired cellular shift *without* prior exptal measures. So could be used for virtual cmpds.
First idea might be to predict predicted gene expression of molecules and then use those instead of exptal measures. But doesn't work very well.
Could we rank cmpds according to rel likelihood to match a target signature instead? Less importance placed on single point-prediction accuracy. Get set of cmpds with increased likelihood to contain the best candidate.
How to do it? Variable Nearest Nbrs (v-NN); a variation of kNN. Liu et al JCIM 2017, 57, 2194. As closest nbrs might not be close, v-NN uses a hard threshold for distance - mean is weighted by distance. This is a more controlled way of doing predictions.
How robust is the signature is based on repeated replicates. Gives standard deviation.
Tested with LINCS data, 19.5K cmpds. Also on internal Novartis PANOMICS data and 215K cmpds.
Refers to Martin et al JCIM 2017 57 2077 regarding pQSAR profile. Predicted assay profile based on RF model of past results.
Shortlisting on similarity to query *and* reliability. Shows effect of relaxing reliability criteria on ability to find known answers. (Ed: she should perhaps try a 2D heat map of reliability threshold vs similarity vs results)
Applicability domain: In a prospective sense, we don't know how many nbrs to include. Can we separate higher ranking from lower ranking queries? Shows heat map giving overview of the results.
I think this is the first time I heard it. I missed a small no of talks.
A new transcriptomics signature shortlisting procedure suitable for screening in large scale libraries. "1 cmpd - > gene expression" shifted to "gene expression -> N cmpds".
unroll
