Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhenkun Gou
dblp:63/3923
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
protein function prediction |
0.1 | 1 | 2007 | 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 |
0.1 | 1 | 2007 | 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 |
0.0 | 1 | 2007 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteinsabstractUNLABELLED: 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 Networks | 1 |
| 2000 | Generalised Canonical Correlation Analysis
Zhenkun Gou, Colin Fyfe |
IDEAL | 1 |