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Rein Aasland

dblp:26/5718 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2002
—ORCID · unresolved

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
association rule mining
0.012002
Association of nucleotide patterns with gene function classes: application to human 3' untranslated sequences · Bioinform. 2002
Bioinformatics and computational biology
data mining
0.012002
Association of nucleotide patterns with gene function classes: application to human 3' untranslated sequences · Bioinform. 2002
Bioinformatics and computational biology › gene regulation
regulatory element discovery
0.012002
Association of nucleotide patterns with gene function classes: application to human 3' untranslated sequences · Bioinform. 2002

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

association rule mining · 0.0
YearPublicationVenuePosition
2002 Association of nucleotide patterns with gene function classes: application to human 3' untranslated sequences
abstract
MOTIVATION: Gene expression is dependent on two main types of signals; one involving transcription factors which initiates gene transcription, and another which regulates the translation of a nascent mRNA. These post-transcriptional events play an important yet incompletely understood role in regulating gene expression and cellular behavior. Many of the identified cis acting elements for translational regulation occur within the 3'untranslated region (3' UTR), and some have been observed to occur with surprising regularity within certain protein function classes. RESULTS: In this study, we present a new association rule mining method for discovering nucleotide sequence patterns that appear in more sequences than expected within protein function classes. The method is applied to a database of human 3' UTR sequences, and some significant associations between nucleotide patterns and protein function classes are discovered. Among previously identified patterns, the AU-Rich Element (ARE) is found here to occur within the 3' UTR of cytokines, providing statistical validation of an association often reported in the literature. The method has also identified some GC-rich patterns, found to occur within the 3' UTR of homeodomain transcription factors and nuclear proteins. The method should be applicable to many types of regulatory element discovery.
Darrell Conklin, Inge Jonassen, Rein Aasland, William R. Taylor
Bioinform.3