Machine Learning Applications

**3**

**1. Introduction**

**Chapter 1**

**Abstract**

Designing Data-Driven Learning

Algorithms: A Necessity to Ensure

Effective Post-Genomic Medicine

*Gaston K. Mazandu, Irene Kyomugisha, Ephifania Geza,* 

Advances in sequencing technology have significantly contributed to shaping the area of genetics and enabled the identification of genetic variants associated with complex traits through genome-wide association studies. This has provided insights into genetic medicine, in which case, genetic factors influence variability in disease and treatment outcomes. On the other side, the missing or hidden heritability has suggested that the host quality of life and other environmental factors may also influence differences in disease risk and drug/treatment responses in genomic medicine, and orient biomedical research, even though this may be highly constrained by genetic capabilities. It is expected that combining these different factors can yield a paradigm-shift of personalized medicine and lead to a more effective medical treatment. With existing "big data" initiatives and high-performance computing infrastructures, there is a need for data-driven learning algorithms and models that enable the selection and prioritization of relevant genetic variants (post-genomic medicine) and trigger effective translation into clinical practice. In this chapter, we survey and discuss existing machine learning algorithms and postgenomic analysis models supporting the process of identifying valuable markers.

**Keywords:** learning algorithms, machine learning, genome-wide association study,

Advancements in the human deoxyribonucleic acid (DNA) microarray and genome sequencing technology have resulted in an exponential growth of publicly available and accessible biological datasets [1, 2]. These "big data" are being explored to systematically uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. Considering current data delude and relatively increased computing power, it is becoming possible to accurately infer desirable features from such data. This highlights the need for efficient learning algorithms to process these data for knowledge discovery by identifying pertinent patterns related to the comparison and classification of different features in these datasets. These learning algorithms should enable

genomic medicine, biomedical research, post-genomic analysis

*Milaine Seuneu, Bubacarr Bah and Emile R. Chimusa*

and Biomedical Research
