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Improved general regression network for protein domain boundary prediction
Authors
A Ceroni
A Vieira
+44 more
Abdur R Sikder
AK Jain
Albert Y Zomaya
AR Sikder
AR Sikder
Bing Bing Zhou
C Chothia
C Civera
CC Lee
CR Robinson
DB Wetlaufer
FMG Pearl
G Pollastri
G Pollastri
HC Van Leeuwen
HM Berman
J Chen
J Cheng
J Liu
J Sim
JCB Melo
JE Gewehr
JS Richardson
JSR Jang
M Dumontier
M Dumontier
M Suyama
MJ Lehtinen
N Nagarajan
OV Galzitskaya
P Baldi
P Bork
Paul D Yoo
RA George
RE Schapire
RL Marsden
RR Copley
RR Joshi
RS Gokhale
S Prompramote
S Veretnik
SF Altschul
TA Holland
Y Freund
Publication date
13 February 2008
Publisher
BioMed Central
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on
PubMed
Abstract
Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd
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Last time updated on 01/04/2019
Michigan Technological University
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