TY - JOUR
T1 - Machine learning in metastatic cancer research
T2 - Potentials, possibilities, and prospects
AU - Petinrin, Olutomilayo Olayemi
AU - Saeed, Faisal
AU - Toseef, Muhammad
AU - Liu, Zhe
AU - Basurra, Shadi
AU - Muyide, Ibukun Omotayo
AU - Li, Xiangtao
AU - Wong, Ka-Chun
N1 - Funding Information:
Side-Out Foundation Metastatic Breast Cancer Database captures data from studies sponsored by the foundation. The database with clinical trial numbers (NCT01074814, NCT01919749, NCT03195192) contains more than 700 data fields. It consists of NGS-based whole/targeted exome sequencing generated genomic data, RNA microarray or RNA Seq generated transcript analysis data, Reverse Phase Protein Microarray (RPPA) generated phosphoproteomic data. Patients are de-identified, and information such as treatment history, demographics, pathological and clinical information, information about metastatic lesions, and outcome data are collected during the trials.
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/3/29
Y1 - 2023/3/29
KW - Cancer metastasis; Data inequality; Deep learning; Early detection; Machine learning; Metastatic cancer
UR - http://www.scopus.com/inward/record.url?scp=85151502493&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151502493&partnerID=8YFLogxK
U2 - 10.1016/j.csbj.2023.03.046
DO - 10.1016/j.csbj.2023.03.046
M3 - Article
AN - SCOPUS:85151502493
SN - 2001-0370
VL - 21
SP - 2454
EP - 2470
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
IS - 2023
ER -