Search: onr:"swepub:oai:DiVA.org:oru-104507" >
Deep learning facil...
Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
-
- Mathema, Vivek Bhakta (author)
- 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
-
- Sen, Partho, 1983- (author)
- Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
-
- Lamichhane, Santosh (author)
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
-
show more...
-
- Oresic, Matej, 1967- (author)
- Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
-
- Khoomrung, Sakda (author)
- 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; Center of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok, Thailand
-
show less...
-
(creator_code:org_t)
- Elsevier, 2023
- 2023
- English.
-
In: Computational and Structural Biotechnology Journal. - : Elsevier. - 2001-0370. ; 21, s. 1372-1382
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- Cancer
- Deep learning
- Oncogene
- Precision medicine
- Reinforcement learning
- Systems medicine
Publication and Content Type
- ref (subject category)
- for (subject category)
Find in a library
To the university's database