Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

被引:40
作者
Al-Tashi, Qasem [1 ]
Saad, Maliazurina B. [1 ]
Muneer, Amgad [1 ]
Qureshi, Rizwan [1 ]
Mirjalili, Seyedali [2 ,3 ,4 ]
Sheshadri, Ajay [5 ]
Le, Xiuning [6 ]
Vokes, Natalie I. [6 ]
Zhang, Jianjun [6 ]
Wu, Jia [1 ,6 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Imaging Phys, Houston, TX 77030 USA
[2] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimizat, Brisbane, Qld 4006, Australia
[3] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea
[4] Obuda Univ, Univ Res & Innovat Ctr, H-1034 Budapest, Hungary
[5] Univ Texas MD Anderson Canc Ctr, Dept Pulm Med, Houston, TX 77030 USA
[6] Univ Texas MD Anderson Canc Ctr, Dept Thorac Head & Neck Med Oncol, Houston, TX 77030 USA
基金
美国国家卫生研究院;
关键词
personalized medicine; biomarker discovery; predictive biomarker; prognostic biomarker; subgroup identification; machine learning; deep learning; feature selection; SUBGROUP IDENTIFICATION; FEATURE-SELECTION; VALIDATION;
D O I
10.3390/ijms24097781
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.
引用
收藏
页数:42
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