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Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand; Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
Örebro University, School of Medical Sciences. Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.ORCID iD: 0000-0003-0475-2763
Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.
Örebro University, School of Medical Sciences. Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.ORCID iD: 0000-0002-2856-9165
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2023 (English)In: Computational and Structural Biotechnology Journal, E-ISSN 2001-0370, Vol. 21, p. 1372-1382Article, review/survey (Refereed) Published
Abstract [en]

Cancer progression is linked to gene-environment interactions that alter cellular homeostasis. The use of biomarkers as early indicators of disease manifestation and progression can substantially improve diagnosis and treatment. Large omics datasets generated by high-throughput profiling technologies, such as microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry, have enabled data-driven biomarker discoveries. The identification of differentially expressed traits as molecular markers has traditionally relied on statistical techniques that are often limited to linear parametric modeling. The heterogeneity, epigenetic changes, and high degree of polymorphism observed in oncogenes demand biomarker-assisted personalized medication schemes. Deep learning (DL), a major subunit of machine learning (ML), has been increasingly utilized in recent years to investigate various diseases. The combination of ML/DL approaches for performance optimization across multi-omics datasets produces robust ensemble-learning prediction models, which are becoming useful in precision medicine. This review focuses on the recent development of ML/DL methods to provide integrative solutions in discovering cancer-related biomarkers, and their utilization in precision medicine.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 21, p. 1372-1382
Keywords [en]
Cancer, Deep learning, Oncogene, Precision medicine, Reinforcement learning, Systems medicine
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:oru:diva-104507DOI: 10.1016/j.csbj.2023.01.043ISI: 000933992600001PubMedID: 36817954Scopus ID: 2-s2.0-85147607641OAI: oai:DiVA.org:oru-104507DiVA, id: diva2:1739231
Note

Funding agencies:

Chalermphrakiat Grant from the Faculty of Medicine, Siriraj Hospital

Mahidol University R016420001

NSRF B16F640099

Available from: 2023-02-24 Created: 2023-02-24 Last updated: 2023-03-15Bibliographically approved

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Sen, ParthoOresic, Matej

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