Machine learning in metastatic cancer research: Potentials, possibilities, and prospects

Olutomilayo Olayemi  Petinrin; Faisal Saeed; Muhammad  Toseef; Zhe  Liu; Shadi Basurra; Ibukun Omotayo  Muyide; Xiangtao   Li; Ka-Chun  Wong

doi:10.1016/j.csbj.2023.03.046

Machine learning in metastatic cancer research: Potentials, possibilities, and prospects

Olutomilayo Olayemi Petinrin, Faisal Saeed, Muhammad Toseef, Zhe Liu, Shadi Basurra, Ibukun Omotayo Muyide, Xiangtao Li, Ka-Chun Wong^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

14 Citations (SciVal)

Original language	English
Pages (from-to)	2454-2470
Number of pages	17
Journal	Computational and Structural Biotechnology Journal
Volume	21
Issue number	2023
DOIs	https://doi.org/10.1016/j.csbj.2023.03.046 https://doi.org/10.1016/j.csbj.2023.03.046
Publication status	Published (VoR) - 29 Mar 2023

Funding

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.

Keywords

Cancer metastasis; Data inequality; Deep learning; Early detection; Machine learning; Metastatic cancer

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title = "Machine learning in metastatic cancer research: Potentials, possibilities, and prospects",

keywords = "Cancer metastasis; Data inequality; Deep learning; Early detection; Machine learning; Metastatic cancer",

author = "Petinrin, \{Olutomilayo Olayemi\} and Faisal Saeed and Muhammad Toseef and Zhe Liu and Shadi Basurra and Muyide, \{Ibukun Omotayo\} and Xiangtao Li and Ka-Chun Wong",

note = "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: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = mar,

day = "29",

doi = "10.1016/j.csbj.2023.03.046",

language = "English",

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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 - https://www.scopus.com/pages/publications/85151502493

UR - https://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

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VL - 21

SP - 2454

EP - 2470

JO - Computational and Structural Biotechnology Journal

JF - Computational and Structural Biotechnology Journal

IS - 2023

ER -

Machine learning in metastatic cancer research: Potentials, possibilities, and prospects

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