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Romain Blanc-Mathieu

dblp:276/0950 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis
profile hidden markov model
0.412020
KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold · Bioinform. 2020
Bioinformatics and computational biology
protein function prediction
0.412020
KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold · Bioinform. 2020

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

profile hidden markov model · 0.4adaptive score threshold · 0.4
YearPublicationVenuePosition
2020 KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold
abstract
SUMMARY: KofamKOALA is a web server to assign KEGG Orthologs (KOs) to protein sequences by homology search against a database of profile hidden Markov models (KOfam) with pre-computed adaptive score thresholds. KofamKOALA is faster than existing KO assignment tools with its accuracy being comparable to the best performing tools. Function annotation by KofamKOALA helps linking genes to KEGG resources such as the KEGG pathway maps and facilitates molecular network reconstruction. AVAILABILITY AND IMPLEMENTATION: KofamKOALA, KofamScan and KOfam are freely available from GenomeNet (https://www.genome.jp/tools/kofamkoala/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Takuya Aramaki, Romain Blanc-Mathieu, Hisashi Endo, Koichi Ohkubo, Minoru Kanehisa, Susumu Goto, Hiroyuki Ogata
Bioinform.2