Precision assessment of some supervised and unsupervised algorithms for genotype discrimination in the genus \textit{pisum} using SSR molecular data

DOI10.1016/J.JTBI.2015.01.001zbMath1405.92220OpenAlexW2045554389WikidataQ30884554 ScholiaQ30884554MaRDI QIDQ1664568

Mansour Ebrahimi, Jaber Nasiri, Amir Hossein Kayvanjoo, Mohammad Reza Naghavi, Mojtaba Nasiri

Publication date: 27 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.jtbi.2015.01.001

zbMATH Keywords

data miningmachine learningDNA fingerprintinggenus \textit{Pisum}SSR markers

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20) Computational methods for problems pertaining to biology (92-08)

Uses Software

• STRUCTURE
• iRSpot-PseDNC
• iNitro-Tyr
• iNuc-PhysChem
• iEzy-drug
• iCDI-PseFpt
• BAPS 2

Cites Work

• Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition
• Some remarks on protein attribute prediction and pseudo amino acid composition
• Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping
• Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction
• Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology
• iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints
• Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via ames test

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