AN EMPIRICAL SCIENCE RESEARCH ON BIOINFORMATICS IN MACHINE LEARNING

Authors:

Sindhu V,Nivedha S,Prakash M,

DOI NO:

https://doi.org/10.26782/jmcms.spl.7/2020.02.00006

Keywords:

Abstract

The subset of Artificial Intelligence (AI) is Machine Learning.  Machine Learning (ML) has a rapid growth in all fields of research such as medical, bio-surveillance, robotics and all other industrial applications. Improvements in accuracy and efficiency of ML techniques in bio-informatics have steadily increased for solving problems in medicine. The aim of this paper is to give brief note about applications of ML in bio-informatics and science research. Bioinformatics involves the interaction of biology, computer science and statistics. In bioinformatics, Data were extracted, analyzed and classified for the prediction of various diseases. This process is time consuming and expensive. To reduce the cost and time, traditional techniques for extracting and analyzing the data were replaced by machine learning techniques.

Refference:

I. Aerts, S., Van Loo, P., Moreau, Y., & De Moor, B. (2004). A genetic algorithm for the detection of new cis-regulatory modules in sets of coregulated genes. Bioinformatics, 20(12), 1974-1976.
II. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., …&Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8(3), 292.
III. Alpaydin, E. (2009). Introduction to machine learning. MIT press.
IV. Bhaskar, H., Hoyle, D. C., & Singh, S. (2006). Machine learning in bioinformatics: A brief survey and recommendations for practitioners. Computers in biology and medicine, 36(10), 1104-1125.
V. Bockhorst, J., Craven, M., Page, D., Shavlik, J., &Glasner, J. (2003). A Bayesian network approach to operon prediction. Bioinformatics, 19(10), 1227-1235.
VI. Buskirk, T. D., Kirchner, A., Eck, A., &Signorino, C. S. (2018). An introduction to machine learning methods for survey researchers. Survey Practice, 11(1), 2718.
VII. Das, K., &Behera, R. N. (2017). A survey on machine learning: concept, algorithms and applications. International Journal of Innovative Research in Computer and Communication Engineering, 5(2), 1301-1309.
VIII. Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., …&Schlaefer, N. (2010). Building Watson: An overview of the DeepQA project. AI magazine, 31(3), 59-79.
IX. Gentleman, R., Carey, V., Huber, W., Irizarry, R., &Dudoit, S. (Eds.). (2006). Bioinformatics and computational biology solutions using R and Bioconductor. Springer Science & Business Media.
X. Inza, I., Calvo, B., Armañanzas, R., Bengoetxea, E., Larrañaga, P., & Lozano, J. A. (2010). Machine learning: an indispensable tool in bioinformatics. In Bioinformatics methods in clinical research (pp. 25-48). Humana Press.
XI. Kaur, S., & Jindal, S. (2016). A survey on machine learning algorithms. Int J Innovative Res AdvEng (IJIRAE), 3(11), 2349-2763.
XII. Larranaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., …& Robles, V. (2006). Machine learning in bioinformatics. Briefings in bioinformatics, 7(1), 86-112.
XIII. Mathé, C., Sagot, M. F., Schiex, T., &Rouzé, P. (2002). Current methods of gene prediction, their strengths and weaknesses. Nucleic acids research, 30(19), 4103-4117.
XIV. Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity.
http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation.
XV. Mitra, S., Datta, S., Perkins, T., &Michailidis, G. (2008). Introduction to machine learning and bioinformatics. Chapman and Hall/CRC.
XVI. Parmigiani, G., Garrett, E. S., Irizarry, R. A., &Zeger, S. L. (2003). The analysis of gene expression data: an overview of methods and software. In The analysis of gene expression data (pp. 1-45). Springer, New York, NY.
XVII. Sapp, C. E. (2017). Preparing and architecting for machine learning. Gartner Technical Professional Advice, 1-37.
XVIII. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

View | Download