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Marina Alexandersson

dblp:16/1219 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—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 › phylogenetics › phylogenetic inference › statistical alignment
pair hidden markov model		 0.012001
Applications of generalized pair hidden Markov models to alignment and gene finding problems · RECOMB 2001
Bioinformatics and computational biology
sequence alignment		 0.012001
Applications of generalized pair hidden Markov models to alignment and gene finding problems · RECOMB 2001
Bioinformatics and computational biology › genome annotation
gene prediction		 0.012001
Applications of generalized pair hidden Markov models to alignment and gene finding problems · RECOMB 2001

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

viterbi algorithm · 0.0generalized pair hidden markov model · 0.0
YearPublicationVenuePosition
2001 Applications of generalized pair hidden Markov models to alignment and gene finding problems
abstract
Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene finding and annotation. Alignment problems can be solved with pair HMMs, while gene finding programs rely on generalized HMMs in order to model exon lengths. In this paper we introduce the generalized pair HMM (GPHMM), which is an extension of both pair and generalized HMMs. We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene finding, and describe applications to DNA-cDNA and DNA-protein alignment. GPHMMs provide a unifying and probabilistically sound theory for modeling these problems.
Lior Pachter, Marina Alexandersson, Simon Cawley
 RECOMB2