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Zhenkun Gou

dblp:63/3923 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 87% Computational science and engineering · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein function prediction
0.112007
DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins · Bioinform. 2007
Bioinformatics and computational biology › sequence analysis
sequence-based prediction
0.112007
DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins · Bioinform. 2007
Computational science and engineering › scientific software
web server
0.012007
DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins · Bioinform. 2007

Methods — techniques the papers use, named apart from their topics

support vector machine · 0.1penalized logistic regression · 0.1kernel logistic regression · 0.1PSI-BLAST PSSM · 0.1
YearPublicationVenuePosition
2007 DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins
abstract
UNLABELLED: This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence. AVAILABILITY: Freely available at http://lcg.rit.albany.edu/dp-bind. SUPPLEMENTARY INFORMATION: http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html.
Seungwoo Hwang, Zhenkun Gou, Igor B. Kuznetsov
Bioinform.2
2004 A canonical correlation neural network for multicollinearity and functional data
Zhenkun Gou, Colin Fyfe
Neural Networks1
2000 Generalised Canonical Correlation Analysis
Zhenkun Gou, Colin Fyfe
IDEAL1