Abstract
Precision medicine and omics technologies play a critical role in the development and implementation of existing biostatistics and bioinformatics approaches. Investments in the development of data mining algorithms and their application to health data are increasing rapidly. It is possible to reveal the relationships between omic levels in order to evaluate all the omic data obtained in the field of biostatistics and bioinformatics, to determine the biomarkers for the diagnosis and treatment of diseases, to provide detailed disease pathophysiology and personalized treatment options. However, there are many challenges to overcome in identifying relationships in big data, such as high dimensionality, heterogeneity, and highly correlated genomic features. Numerous methods using statistics, machine learning and artificial intelligence tools to address these issues are currently being developed and investigated for omic data analysis.
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Cephe, A. et al. (2023). Bioinformatics and Biostatistics in Precision Medicine. In: Tuli, H.S., Yerer Aycan, M.B. (eds) Oncology: Genomics, Precision Medicine and Therapeutic Targets. Springer, Singapore. https://doi.org/10.1007/978-981-99-1529-3_8
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