title: Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework.
abstract: Effective prioritization of chemical compounds that show promising bioactivities from compound screenings represents a first critical step toward identifying successful drug candidates. Current development on computational approaches for compound prioritization is largely focused on devising advanced ranking algorithms that better learn the ordering among compounds. However, such methodologies are fundamentally limited by the scarcity of available data, particularly when the screenings are conducted at a relatively small scale over known promising compounds. Instead, in this work, we explore the structures of bioassay space and leverage such structures to improve ranking performance of an existing strong ranking algorithm. This is done by identifying assistance bioassays and assistance compounds intelligently and leveraging such assistance within the existing ranking algorithm. By leveraging the assistance bioassays and assistance compounds, the data scarcity can be properly compromised. Along this line, we develop a suite of assistance bioassay selection methods and assistance compound selection methods. Our experiments demonstrate an overall 8.34% improvement on the ranking performance over the state of the art.
authors.affiliation: Indiana University-Purdue University, Indianapolis , 723 West Michigan St., SL 280, Indianapolis, Indiana 46202, United States.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Liu
authors.affiliation: Indiana University-Purdue University, Indianapolis , 723 West Michigan St., SL 280, Indianapolis, Indiana 46202, United States.|Center for Computational Biology and Bioinformatics, Indiana University School of Medicine , 410 West 10th St., HITS 5000, Indianapolis, Indiana 46202, United States.
authors.forename: Xia
authors.identifier: 0000-0002-6842-1165
authors.initials: X
authors.lastname: Ning
mesh_terms: D001681:Biological Assay; D003198:Computer Simulation; D055808:Drug Discovery; D000069550:Machine Learning; D013997:Time Factors