**Author details**

Gaston K. Mazandu1,2,3\*, Irene Kyomugisha2,4, Ephifania Geza2,3, Milaine Seuneu2 , Bubacarr Bah2,4 and Emile R. Chimusa1

1 Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town (UCT), Cape Town, South Africa

2 African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa

3 Computational Biology (CBIO) Division, Department of Integrative Biomedical Science, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town (UCT), Cape Town, South Africa

4 Division of Applied Mathematics, Department of Mathematical Sciences, University of Stellenbosch, Stellenbosch, South Africa

\*Address all correspondence to: kuzamunu@aims.ac.za

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**15**

*Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic…*

[10] Hartigan JA, Wong MA. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society,

[11] Bothe MK, Dickens L, Reichel K, Tellmann A, Ellger B, Westphal M, et al. The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas. Expert Review of Medical Devices. 2013;**10**(5):661-673

[12] Weng WH, Gao M, He Z, Yan S, Szolovits P. Representation and

Beach, CA, USA. 2017

Processing. 2017. pp. 895-905

[15] Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, et al. Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface. 2018;**15**. DOI: 10.1098/

[16] Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology. 2015;**33**(8):831-838

rsif.2017.0387

reinforcement learning for personalized glycemic control in septic patients. In: 31st Conference on Neural Information Processing Systems (NIPS); Long

[13] Ling Y, Hasan SA, Datla V, Qadir A, Lee K, Liu J, et al. Learning to diagnose: assimilating clinical narratives using deep reinforcement learning. In: Proceedings of the 8th International Joint Conference on Natural Language

[14] Nemati S, Ghassemi MM, Clifford GD. Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2016. pp. 2978-2981. DOI: 10.1109/EMBC.2016.7591355

Series C. 1979;**28**(1):100-108

*DOI: http://dx.doi.org/10.5772/intechopen.84148*

[1] Geza E, Mugo J, Mulder NJ, Wonkam A, Chimusa ER, Mazandu GK. A comprehensive survey of models for dissecting local ancestry deconvolution

in human genome. Briefings in Bioinformatics. 2018:1-16

[2] Mazandu GK, Chimusa ER, Mulder NJ. Gene ontology semantic similarity tools: Survey on features and challenges for biological knowledge discovery. Briefings in Bioinformatics.

[3] Strobl C, Malley J, Gerhard T. Characteristics of classification and regression trees, bagging and random forests. Psychological Methods.

[4] Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician.

[6] Cortes C, Vapnik V. Supportvector networks. Machine Learning.

[7] Breiman L. Random forests. Machine

[8] Karpathy A. CS231n Convolutional Neural Networks for Visual Recognition. Available from: http://cs231n.github.io/

[9] Murtagh F, Contreras P. Algorithms for hierarchical clustering: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2012;**2**:86-97. 7 (2017) e1219

[5] Khondoker M, Dobson R, Skirrow C, Simmons A, Stahl D. A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies. Statistical Methods in Medical Research. 2016;**25**(5):1804-1823

2016;**18**(5):886-901

2009;**14**(4):323-348

1992;**46**(3):175-185

1995;**20**(3):273-297

Learning. 2001;**45**(1):5-32

neural-networks-1/#nn 2017

**References**

*Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic… DOI: http://dx.doi.org/10.5772/intechopen.84148*
