Multiple machine learning algorithms, validation of external clinical cohort and assessments of model gain effects will better serve cancer research on bioinformatic models.

IF 5.3 2区医学 Q1 ONCOLOGY

Cancer Cell International Pub Date : 2024-12-23 DOI:10.1186/s12935-024-03601-0

Fangshi Xu, Zongyu Li, Hao Guan, Jiancang Ma

引用次数: 0

Abstract

Bioinformatics models greatly contribute to individualized assessments of cancer patients. However, considerable research neglected some critical technological points, including comparisons of multiple modeling algorithms, evaluating gain effects of constructed model, comprehensive bioinformatics analyses and validation of clinical cohort. These issues are worthy of emphasizing, which will better serve future cancer research.

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来源期刊

Cancer Cell International ONCOLOGY-

CiteScore

10.90

自引率

1.70%

发文量

360

审稿时长

1 months

期刊介绍： Cancer Cell International publishes articles on all aspects of cancer cell biology, originating largely from, but not limited to, work using cell culture techniques. The journal focuses on novel cancer studies reporting data from biological experiments performed on cells grown in vitro, in two- or three-dimensional systems, and/or in vivo (animal experiments). These types of experiments have provided crucial data in many fields, from cell proliferation and transformation, to epithelial-mesenchymal interaction, to apoptosis, and host immune response to tumors. Cancer Cell International also considers articles that focus on novel technologies or novel pathways in molecular analysis and on epidemiological studies that may affect patient care, as well as articles reporting translational cancer research studies where in vitro discoveries are bridged to the clinic. As such, the journal is interested in laboratory and animal studies reporting on novel biomarkers of tumor progression and response to therapy and on their applicability to human cancers.