Abstract
A major milestone in modern biology was the complete sequencing of the human genome. But it produced a whole set of new challenges in exploring the functions and interactions of different parts of the genome. One application is predicting disorders based on mining the genotype and understanding how the interactions between genetic loci lead to certain human diseases.
However, typically disease phenotypes are genetically complex. They are characterized by large, high-dimensional data sets. Also, usually the sample size is small.
Recently machine learning and predictive modeling approaches have been successfully applied to understand the genotype-phenotype relations and link them to human diseases. They are well suited to overcome the problems of the large data sets produced by the human genome and its high-dimensionality. Machine learning techniques have been applied in virtually all data mining domains and have proven to be effective in BioData mining as well.
This paper describes some of the techniques that have been adopted in recent studies in human genome analysis.
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Wlodarczak, P., Soar, J., Ally, M. (2015). Genome Mining Using Machine Learning Techniques. In: Geissbühler, A., Demongeot, J., Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Inclusive Smart Cities and e-Health. ICOST 2015. Lecture Notes in Computer Science(), vol 9102. Springer, Cham. https://doi.org/10.1007/978-3-319-19312-0_39
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DOI: https://doi.org/10.1007/978-3-319-19312-0_39
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