Abstract
Machine learning (ML) is an analytical approach that has been on increasing importance in this field. In this chapter, we would like to highlight the use of ML for disease risk prediction and prognosis to identify the scope of successful applications to date. Despite the enthusiasm, we feel that the evaluation of ML methods in real data sets has been limited thus far. We also feel that machine learning approaches can serve as methods of choice for the integration of the ever more complex data sets being generated in the era of next-generation sequencing.
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Collins, A., Yao, Y. (2018). Machine Learning Approaches: Data Integration for Disease Prediction and Prognosis. In: Yao, Y. (eds) Applied Computational Genomics. Translational Bioinformatics, vol 13. Springer, Singapore. https://doi.org/10.1007/978-981-13-1071-3_10
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DOI: https://doi.org/10.1007/978-981-13-1071-3_10
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