Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine

Vivek Bhakta Mathema, Partho Sen, Santosh Lamichhane, Matej Orešič, Sakda Khoomrung*

*Corresponding author for this work

Research output: Contribution to journalReview Article or Literature Reviewpeer-review

16 Citations (Scopus)
10 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)1372-1382
Number of pages11
JournalComputational and Structural Biotechnology Journal
Publication statusPublished - Jan 2023
MoE publication typeA2 Review article in a scientific journal


  • Cancer
  • Deep learning
  • Oncogene
  • Precision medicine
  • Reinforcement learning
  • Systems medicine


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