Recent advances in machine learning methods for predicting heat shock proteins.

Recent advances in machine learning methods for predicting heat shock proteins.
Curr Drug Metab. 2018 Oct 30;:
Authors: Chen W, Feng P, Liu T, Jin D
Abstract
As molecular chaperones, heat shock proteins (HSPs) not only play key roles in protein folding and maintaining protein stabilities, but also linked with multiple kinds of diseases. Therefore, HSPs have been regarded as the focus of drug design. Since HSPs from different families play distinct functions, accurately classifying the families of HSPs is the key step to clearly understand their biological functions. In contrast to labor-intensive and cost-ineffective experimental methods, accurately computational classification of HSP families has emerged to be an alternative approach. In this work, we reviewed the existing machine learning methods for identifying the different families of HSPs. We hope this work will provide novel insights into the future researches on HSPs.
PMID: 30378494 [PubMed - as supplied by publisher]

Source: Current Drug Metabolism - October 30, 2018 Category: Drugs & Pharmacology Authors: Chen W, Feng P, Liu T, Jin D Tags: Curr Drug Metab Source Type: research