Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen

Abstract

High Content Screening is an important tool in drug discovery and characterisation. Often, high content drug screens are performed on one single cell line. Yet, a single cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterise drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. The contribution of this paper is twofold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimisation of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multitask autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease.

Bladder And Kidney Cancer Genome Classification Using Neural Network

“…SNP based investigations are the most normal ones using this methodology, anyway most investigations do not have a cross-study validation part across at any rate two free considers. Here they investigate the cross-validation and cross-study validation of anticipating kidney cancer case and controls with SNPs got from entire exome arrangements at the National Cancer Institute [7]. From the Genomics Data Commons entryway they got adjusted entire exome arrangements of two unique kidney cancer considers: 110 cases and controls of KIRP for renal papillary cell carcinoma and 34 cases and controls of KICH for kidney chromophobe cell carcinoma.…”