Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale.

Sarkar, Jnanendra Prasad; Saha, Indrajit; Lancucki, Adrian; Ghosh, Nimisha; Wlasnowolski, Michal; Bokota, Grzegorz; Dey, Ashmita; Lipinski, Piotr; Plewczynski, Dariusz

Sarkar, Jnanendra Prasad; Saha, Indrajit; Lancucki, Adrian; Ghosh, Nimisha; Wlasnowolski, Michal; Bokota, Grzegorz; Dey, Ashmita; Lipinski, Piotr; Plewczynski, Dariusz.

Afiliación

Sarkar JP; Data, Analytics & AI, Larsen & Toubro Infotech Ltd., Pune, India.
Saha I; Department of Computer Science & Engineering, Jadavpur University, Kolkata, India.
Lancucki A; Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata, India.
Ghosh N; Computational Intelligence Research Group, Institute of Computer Science, University of Wroclaw, Wroclaw, Poland.
Wlasnowolski M; Department of Computer Science and Information Technology, SOA University, Bhubaneshwar, India.
Bokota G; Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
Dey A; Institute of Informatics, University of Warsaw, Warsaw, Poland.
Lipinski P; Centre of New Technologies, University of Warsaw, Warsaw, Poland.
Plewczynski D; Department of Computer Science & Engineering, Jadavpur University, Kolkata, India.

Front Genet ; 11: 982, 2020.

Article en En | MEDLINE | ID: mdl-33281862

Palabras clave

KEGG pathway; cancer; cox regression; feature selection; gene ontology; machine learning; next generation sequencing; stochastic neighbor embedding

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Front Genet Año: 2020 Tipo del documento: Article País de afiliación: India

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