Prospects and Challenges of Machine learning based Research in Bioinformatics: The Case for Africa

Bioinformatics, a field of nanobiotechnology, employs mathematical and statistical algorithms in studying biological data. Bioinformatics is also at the service of nanomedicine. Methods adopted in bioinformatics are typically software based and currently involve machine learning approaches which, when successful, are far more accurate in biological data analysis or prediction than traditional software-based methods. Considering a wide scope of diseases that could be combatted through machine learning-based bioinformatics research, the first goal of this paper is to highlight the prospects for research on resisting diseases peculiar to Nigeria and Africa at large, such as, improving the genetic resistance to malaria; eradicating diabetes disease, sickle cell anemia; and improving genetic resistance to HIV/AIDS. The second goal of this paper shows how machine learning can be applied in key areas of bioinformatics. Many researchers newly introduced to the field of bioinformatics often find the bioinformatics tools very confusing. As such, the second goal of this paper would help such researchers understand why, where and how to apply machine learning in bioinformatics research.