mSRFR: a machine learning model using microalgal signature features for ncRNA classification.

Anuntakarun, Songtham; Lertampaiporn, Supatcha; Laomettachit, Teeraphan; Wattanapornprom, Warin; Ruengjitchatchawalya, Marasri

Anuntakarun, Songtham; Lertampaiporn, Supatcha; Laomettachit, Teeraphan; Wattanapornprom, Warin; Ruengjitchatchawalya, Marasri.

Afiliação

Anuntakarun S; Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, 10150, Thailand.
Lertampaiporn S; School of Information Technology, KMUTT, Bang Mod, Thung Khru, Bangkok, 10140, Thailand.
Laomettachit T; Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency at King Mongkut's University of Technology Thonburi, Bang Khun Thian, Bangkok, 10150, Thailand.
Wattanapornprom W; Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, 10150, Thailand.
Ruengjitchatchawalya M; Department of Mathematics, Faculty of Science, KMUTT, Bangkok, 10140, Thailand.

BioData Min ; 15(1): 8, 2022 Mar 21.

Article em En | MEDLINE | ID: mdl-35313925

Palavras-chave

Machine learning; Microalgae; Non-coding RNAs; Random Forest; SMOTE; Signature feature

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: BioData Min Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Tailândia

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