Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method

DOI10.3934/mbe.2021167zbMath1471.92230OpenAlexW3155449447MaRDI QIDQ1984068

Hui Ding, Kyle Hippe, Rida Sarwar Khan, Renzhi Cao, Cassandra Hunt, Hasan Zulfiqar, Xiao-Ming Song, Farwa Hassan

Publication date: 13 September 2021

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2021167

zbMATH Keywords

feature extractionfeature selectionrandom forestDNA modificationN4-methylcytosine

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20) Genetics and epigenetics (92D10)

Related Items (1)

model_4mc

Uses Software

• Cd-hit
• GitHub
• mRMRe
• PseKRAAC
• iBLP
• XG-PseU
• ACPred
• DeepTorrent
• TargetAntiAngio
• 4mCPred
• DNA4mC-LIP
• i4mC-Mouse
• iMRM
• BioSeq-Analysis
• IDP-Seq2Seq
• iBitter-SCM
• iTTCA-Hybrid
• Meta-iAVP
• Meta-iPVP
• iCircDA-MF
• RaacLogo
• RAACBook
• iTerm-PseKNC
• FoldRec-C2C
• PSAC
• iDNA-MS
• Deep4mC

Cites Work

• Identification of hormone binding proteins based on machine learning methods
• Random forests

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