A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
Tekijät: Mathema Vivek Bhakta, Sen Partho, Lamichhane Santosh, Orešič Matej, Khoomrung Sakda
Kustantaja: Research Network of Computational and Structural Biotechnology
Julkaisuvuosi: 2023
Journal: Computational and Structural Biotechnology Journal
Tietokannassa oleva lehden nimi: Computational and structural biotechnology journal
Lehden akronyymi: Comput Struct Biotechnol J
Vuosikerta: 21
Aloitussivu: 1372
Lopetussivu: 1382
ISSN: 2001-0370
DOI: https://doi.org/10.1016/j.csbj.2023.01.043
Verkko-osoite: https://doi.org/10.1016/j.csbj.2023.01.043
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/178764838
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
