Prediction of success for polymerase chain reactions using the Markov maximal order model and support vector machine

DOI10.1016/J.JTBI.2015.01.017zbMath1406.92194OpenAlexW2079170647WikidataQ47408828 ScholiaQ47408828MaRDI QIDQ1715227

Ping-an He, Xuepeng Li, Yan Yang, Defu Zhang, Wenchao Fei, Jin Zhu, Chun Li, Zhifu Wang, Shumin Yi, Xiaoqing Yu, Changzhong Wang

Publication date: 4 February 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2015.01.017

zbMATH Keywords

Markov chainssupport vector machinesampliconDNA templatepolymerase chain reactions

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Biochemistry, molecular biology (92C40)

Uses Software

• LIBSVM
• iRSpot-TNCPseAAC
• iRSpot-PseDNC
• iSNO-PseAAC

Cites Work

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• Study of proteome maps using partial ordering
• Phylogenetic analysis of DNA sequences based on \(k\)-word and rough set theory
• Support Vector Machines
• A measure of the similarity of sets of sequences not requiring sequence alignment.

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