ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles.

Journal Article (Journal Article)

Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (

Full Text

Duke Authors

Cited Authors

  • Anene, CA; Khan, F; Bewicke-Copley, F; Maniati, E; Wang, J

Published Date

  • June 11, 2021

Published In

Volume / Issue

  • 2 / 6

Start / End Page

  • 100270 -

PubMed ID

  • 34179848

Pubmed Central ID

  • PMC8212143

Electronic International Standard Serial Number (EISSN)

  • 2666-3899

Digital Object Identifier (DOI)

  • 10.1016/j.patter.2021.100270


  • eng

Conference Location

  • United States