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KUPKOVÁ, K. SEDLÁŘ, K. PROVAZNÍK, I.
Originální název
Multidimensional Correlated Mutation Analysis for Protein Contact Map Prediction
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Correlated mutation (CM) analysis has been proved to be an important tool used in protein contact map prediction from primary amino acid sequence. Over the last years CM methods have been refined and then often combined with methods of different nature in order to improve reached precision. However, since the methods are still relatively new, only precision and improvement have been reported without mentioning recall which is very low. In this paper, we combine previously described CM analysis methods with an outcome of four new techniques which significantly increase recall for the cost of minimal precision impairment. This study is the first of our knowledge with focus on recall improvement.
Klíčová slova
Correlated mutations; Contact map; Protein structure prediction; Residue contact
Autoři
KUPKOVÁ, K.; SEDLÁŘ, K.; PROVAZNÍK, I.
Vydáno
26. 5. 2016
Nakladatel
Springer International Publishing
Místo
Německo
ISBN
9783319399034
Kniha
Information Technologies in Medicine, 5th International Conference, ITIB 2016 Kamień Śląski, Poland, June 20 - 22, 2016 Proceedings, Volume 2
ISSN
2194-5357
Periodikum
Advances in Intelligent Systems and Computing
Ročník
472
Číslo
1
Stát
Švýcarská konfederace
Strany od
133
Strany do
144
Strany počet
12
BibTex
@inproceedings{BUT126084, author="Kristýna {Kupková} and Karel {Sedlář} and Valentine {Provazník}", title="Multidimensional Correlated Mutation Analysis for Protein Contact Map Prediction", booktitle="Information Technologies in Medicine, 5th International Conference, ITIB 2016 Kamień Śląski, Poland, June 20 - 22, 2016 Proceedings, Volume 2", year="2016", journal="Advances in Intelligent Systems and Computing", volume="472", number="1", pages="133--144", publisher="Springer International Publishing", address="Německo", doi="10.1007/978-3-319-39904-1\{_}12", isbn="9783319399034", issn="2194-5357" }