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Tanzeem Haque

dblp:132/3537 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—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 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › molecular informatics
cheminformatics
0.212013
HitPick: a web server for hit identification and target prediction of chemical screenings · Bioinform. 2013
Bioinformatics and computational biology › drug discovery › drug screening
hit identification
0.212013
HitPick: a web server for hit identification and target prediction of chemical screenings · Bioinform. 2013
Bioinformatics and computational biology › drug discovery
virtual screening
0.212013
HitPick: a web server for hit identification and target prediction of chemical screenings · Bioinform. 2013

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

naive bayes · 0.2b-score · 0.21-nearest-neighbor · 0.2
YearPublicationVenuePosition
2013 HitPick: a web server for hit identification and target prediction of chemical screenings
abstract
MOTIVATION: High-throughput phenotypic assays reveal information about the molecules that modulate biological processes, such as a disease phenotype and a signaling pathway. In these assays, the identification of hits along with their molecular targets is critical to understand the chemical activities modulating the biological system. Here, we present HitPick, a web server for identification of hits in high-throughput chemical screenings and prediction of their molecular targets. HitPick applies the B-score method for hit identification and a newly developed approach combining 1-nearest-neighbor (1NN) similarity searching and Laplacian-modified naïve Bayesian target models to predict targets of identified hits. The performance of the HitPick web server is presented and discussed. AVAILABILITY: The server can be accessed at http://mips.helmholtz-muenchen.de/proj/hitpick. CONTACT: [email protected].
Ingo Vogt, Tanzeem Haque, Monica Campillos
Bioinform.3