Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia.

Bohannan, Zachary S; Coffman, Frederick; Mitrofanova, Antonina

Bohannan, Zachary S; Coffman, Frederick; Mitrofanova, Antonina.

Afiliação

Bohannan ZS; Rutgers, The State University of New Jersey, School of Health Professions, Department of Health Informatics, 65 Bergen Street, Suite 120, Newark, NJ 07107-1709, United States.
Coffman F; Rutgers, The State University of New Jersey, School of Health Professions, Department of Health Informatics, 65 Bergen Street, Suite 120, Newark, NJ 07107-1709, United States.
Mitrofanova A; Rutgers, The State University of New Jersey, School of Health Professions, Department of Health Informatics, 65 Bergen Street, Suite 120, Newark, NJ 07107-1709, United States.

Comput Struct Biotechnol J ; 20: 583-597, 2022.

Article em En | MEDLINE | ID: mdl-35116134

Palavras-chave

ALL, Acute lymphblastic leukemia; B-ALL, B cell acute lymphoblastic leukemia; Bioinformatics; COG, Children's Oncology Group; Clinical oncology; EFS, Event-free survival; Genomics; MRD, Minimal residual disease; Machine learning; Random survival forest; WBC, White blood cell count

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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