Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
Issued Date
2023-01-01
Resource Type
eISSN
20010370
Scopus ID
2-s2.0-85147607641
Journal Title
Computational and Structural Biotechnology Journal
Volume
21
Start Page
1372
End Page
1382
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computational and Structural Biotechnology Journal Vol.21 (2023) , 1372-1382
Suggested Citation
Mathema V.B., Sen P., Lamichhane S., Orešič M., Khoomrung S. Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine. Computational and Structural Biotechnology Journal Vol.21 (2023) , 1372-1382. 1382. doi:10.1016/j.csbj.2023.01.043 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81798
Title
Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
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.
