Evaluation of machine learning classifiers to predict compound mechanism of action when transferred across distinct cell-lines.

Scott Warchal, John Dawson, Neil Carragher

Research output: Contribution to journalArticlepeer-review

Abstract

Multiparametric high content imaging assays have become established to classify cell phenotypes from functional genomic and small-molecule library screening assays. Several groups have implemented machine learning classifiers to predict the mechanism-of-action of phenotypic hit compounds by comparing similarity of their high content phenotypic profiles with a reference library of well-annotated compounds. However, the majority of such examples are
restricted to a single cell type often selected because of its suitability for simple image analysis and intuitive segmentation of morphological features. The aim of the current study was to evaluate and compare the performance of a classic ensemble based tree classifier trained on extracted morphological features and a deep-learning classifier using convolution neural networks (CNNs) trained directly on images from the same dataset to predict compound
mechanism-of-action across a morphologically and genetically distinct cell panel. Our results demonstrate that application of a CNN classifier delivers equivalent accuracy compared to an ensemble-based tree classifier at compound mechanism of action prediction within cell lines. However, our CNN analysis performs worse than an ensemble based tree classifier when trained
on multiple cell lines at predicting compound mechanism of action on an unseen cell line.
Original languageEnglish
JournalSlas Discovery
Early online date29 Jan 2019
DOIs
Publication statusE-pub ahead of print - 29 Jan 2019

Keywords

  • High content screening
  • cell-based assays
  • cancer and cancer drugs
  • machine learning

Fingerprint

Dive into the research topics of 'Evaluation of machine learning classifiers to predict compound mechanism of action when transferred across distinct cell-lines.'. Together they form a unique fingerprint.

Cite this