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Wen-Shien Huang

dblp:15/5782 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein-protein interaction prediction
0.012004
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome · Bioinform. 2004
Bioinformatics and computational biology › protein analysis › protein-protein interaction
human interactome
0.012004
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome · Bioinform. 2004
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction network analysis
0.012004
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome · Bioinform. 2004

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

subcellular localization integration · 0.0interolog mapping · 0.0co-expression clustering · 0.0
YearPublicationVenuePosition
2004 POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome
abstract
One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.
Tao-Wei Huang, An-Chi Tien, Wen-Shien Huang, Yuan-Chii G. Lee, Chin-Lin Peng, Huei-Hun Tseng, Cheng-Yan Kao, Chi-Ying F. Huang
Bioinform.3