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Mark Boguski

dblp:36/563 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 2

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
2 papers
Bioinformatics and computational biology · 100%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene expression analysis		 0.012000
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis · ISMB 2000
Bioinformatics and computational biology › gene expression analysis
microarray data analysis		 0.012000
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis · ISMB 2000
Bioinformatics and computational biology › sequence alignment
local alignment		 0.011994
A note about computing all local alignments · Comput. Appl. Biosci. 1994
Bioinformatics and computational biology
sequence alignment		 0.011994
A note about computing all local alignments · Comput. Appl. Biosci. 1994
Information retrieval
text analysis		 0.012000
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis · ISMB 2000

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

information retrieval · 0.1dynamic programming · 0.0
YearPublicationVenuePosition
2000 Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis
Hagit Shatkay, Stephen Edwards, W. John Wilbur, Mark Boguski
 ISMB4
1994 A note about computing all local alignments
abstract
A recent paper in this journal by G. Barton proposed an efficient algorithm for locating locally optimal alignments between two sequences. Although the paper claims that all such alignments are found, the approach frequently fails to detect some of the significant matches. This note explains the deficiency.
Webb Miller, Mark Boguski
 Comput. Appl. Biosci.2