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

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 an...

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Published inCancer cell international Vol. 24; no. 1; pp. 427 - 4
Main Authors Xu, Fangshi, Li, Zongyu, Guan, Hao, Ma, Jiancang
Format Journal Article
LanguageEnglish
Published England BioMed Central 23.12.2024
BMC
Subjects
Algorithms
Bioinformatics
Cancer
Cancer research
Cell death
Clinical medicine
Clinical validation
Comment
Decision making
Gain effect
Machine learning
Machine learning algorithm
Medical prognosis
Medical research
Prognostic model
R&D
Regularization methods
Research & development
Support vector machines
Variables
Gain effect
Prognostic model
Clinical validation
Bioinformatics
Machine learning algorithm
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Summary: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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ISSN:1475-2867
1475-2867
DOI:10.1186/s12935-024-03601-0