Building an Essential Gene Classification Framework

machine learning;classification;yeast;morphological alterations;essential genes

Saha, Soma ; Dr. Dennis Bahler, Committee Member,Dr. Xiaosong Ma, Committee Member,Dr. Steffen Heber, Committee Chair,Saha, Soma ; Dr. Dennis Bahler ; Committee Member ; Dr. Xiaosong Ma ; Committee Member ; Dr. Steffen Heber ; Committee Chair

University:North Carolina State University

关键词: machine learning;    classification;    yeast;    morphological alterations;    essential genes;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/2546/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

The analysis of gene deletions is a fundamental approach for investigating gene function. We applied machine learning techniques to predict phenotypic effects of gene deletions in yeast. We created a dataset containing features that potentially have predictive power and then used feature processing techniques to improve the dataset and identify features that are important for our classification problem. We evaluated four different classification algorithms, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest, with respect to this problem. We used our framework to complement the set of experimentally determined essential yeast genes produced by the Saccharomyces Genome Deletion Project and produce more than 2000 annotations for genes that might cause morphological alterations in yeast.