fulltext.study @t Gmail

Machine Learnable Fold Space Representation based on Residue Cluster Classes

Paper ID Volume ID Publish Year Pages File Format Full-Text
14966 1365 2015 7 PDF Available
Title
Machine Learnable Fold Space Representation based on Residue Cluster Classes
Abstract

•We implemented a vectorial representation of residues contacts•We implemented an efficient statistical test for machine-learnable data•Our vectorial model reproduces protein packing•A predictor is trained to effectively reproduce CATH and SCOP classifications•Our predictor automatically identified inconsistent classification in CATH and SCOP

MotivationProtein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using similarity indexes and/or machine learning approaches, each with different limitations.ResultsWe propose a method for constructing a compact vector space model of protein fold space by representing each protein structure by its residues local contacts. We developed an efficient method to statistically test for the separability of points in a space and showed that our protein fold space representation is learnable by any machine-learning algorithm.AvailabilityAn API is freely available at https://code.google.com/p/pyrcc/.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slide

First Page Preview
Machine Learnable Fold Space Representation based on Residue Cluster Classes
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 59, Part A, December 2015, Pages 1–7
Authors
, , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us