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Title |
Comparative Study of Machine Learning Techniques for Population Genetics |
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Author |
Muhammad Arslan Amin, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Mohsin Abbas, Muhammad Haroon Jilani, Usman Nasir, Muhammad Bilal Sarwar, and Hafiz Muhammad Talha |
| Citation |
Vol. 19 No. 6 pp. 78-84 |
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Abstract |
As the size of population genetic data increases, researchers face difficulties in understanding this huge amount of data. In order to work with complex data, computational methods are being developed to work precisely with population genetic data. Various kinds of computational techniques have been developed to analyze population genetic data. Machine learning is a significant area that has considerable potentials for population genetics. Machine learning aims to implement computer algorithms that learn with experience to help humans in the analysis of complex and large data sets. Machine learning is still in its infancy for various problems, especially in the area of evolutionary and population genetics. This study presents machine learning applications in order to investigate the genetic data of population including different concepts that are relevant to population genetics |
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Keywords |
Machine Learning Computer Algorithms Genetic Data Population Genetics Evolutionary Genetics. |
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