MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials

Journal article

Open access

Peer reviewed

Tong Liu and Zheng Wang

BMC bioinformatics, Vol.21(Suppl 4), pp.246-246

2020-07-06

PMID: 32631256

Abstract

Research

Single-model QA

Protein model quality assessment

Random forests

Protein energy potentials

url

https://doi.org/10.1186/s12859-020-3383-3View

Published (Version of record) Open

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Citation topics: 2 Chemistry; 2.123 Protein Stucture, Folding & Modelling; 2.123.13 Protein Folding
Web Of Science research areas: Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology
ESI research areas: Computer Science

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Title: MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials
Creators: Tong Liu - 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL 33124 USA
Zheng Wang - 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL 33124 USA
Publication Details: BMC bioinformatics, Vol.21(Suppl 4), pp.246-246
Publisher: BioMed Central; London
Grant note: R15GM120650 / ;
Academic Unit: College of A&S; A&S - Computer Science
Language: English
Resource Type: Journal article
PMID: 32631256
Record Identifier: 991031576473902976