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Shravan Sukumar

dblp:185/8272 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 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 · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene regulation › binding site prediction
DNA-binding site prediction
0.212016
DBSI server: DNA binding site identifier · Bioinform. 2016
Bioinformatics and computational biology › computational structural biology
protein-nucleic acid interaction
0.212016
DBSI server: DNA binding site identifier · Bioinform. 2016
Bioinformatics and computational biology
protein structure analysis
0.112016
DBSI server: DNA binding site identifier · Bioinform. 2016
Bioinformatics and computational biology › structural bioinformatics
structure-based prediction
0.112016
DBSI server: DNA binding site identifier · Bioinform. 2016

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

support vector machine · 0.2
YearPublicationVenuePosition
2016 DBSI server: DNA binding site identifier
abstract
UNLABELLED: : Protein-nucleic acid interactions are among the most important intermolecular interactions in the regulation of cellular events. Identifying residues involved in these interactions from protein structure alone is an important challenge. Here we introduce the webserver interface to DNA Binding Site Identifier (DBSI), a powerful structure-based SVM model for the prediction and visualization of DNA binding sites on protein structures. DBSI has been shown to be a top-performing model to predict DNA binding sites on the surface of a protein or peptide and shows promise in predicting RNA binding sites. AVAILABILITY AND IMPLEMENTATION: Server is available at http://dbsi.mitchell-lab.org CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Shravan Sukumar, Xiaolei Zhu 0001, Spencer S. Ericksen, Julie C. Mitchell
Bioinform.1