Paper Info
Title
Cspred: A Machine-Learning-Based Compound Model To Identify The Functional Activities Of Biologically-Stable Toxins
Abstract
Pharmaceutical industries are interested in Cysteine-stabilized peptides because they offer an array bioactive properties while being highly stable under a range of physiological conditions. However, it is widely appreciated that only a small fraction of this type of peptides have been experimentally discovered while a large number remain unidentified. However, identification of these cysteine-stabilized peptides using normal sequence alignment is challenging because of the high noise to signal ratio. Therefore, we propose a machine learning-based compound model to predict functional properties of cysteine-stabilized peptides from their primary sequence. We also offer a freely available web-server at http://watson.ecs.baylor.edu/cspred to make the model available to the scientific community.
Year
DOI
Venue
2017
10.1109/BIBM.2017.8218014
2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
DocType
ISSN
Cysteine-stabilized peptides, machine learning, m-NGSG, CSPred
Conference
2156-1125
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
S. M. Ashiqul Islam100.68
Christopher Kearney221.39
Erich J Baker3536.94