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Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

[ Vol. 19 , Issue. 25 ]

Author(s):

Ruirui Liang, Jiayang Xie, Chi Zhang, Mengying Zhang, Hai Huang, Haizhong Huo*, Xin Cao* and Bing Niu*   Pages 2301 - 2317 ( 17 )

Abstract:


In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Keywords:

Big data, Machine learning, Next generation sequencing, High-through sequence, Support vector machine, Naïve Bayes classifier, Artifical neural work, Ensemble learning, Adaboost, bagging.

Affiliation:

School of Life Sciences, Shanghai University, Shanghai, 200444, School of Life Sciences, Shanghai University, Shanghai, 200444, Foshan Huaxia Eye Hospital, Huaxia Eye Hospital Group, Foshan 528000, School of Life Sciences, Shanghai University, Shanghai, 200444, School of Life Sciences, Shanghai University, Shanghai, 200444, Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200011, Zhongshan Hospital, Institute of Clinical Science, Shanghai Medical College, Fudan University, Shanghai 200032, School of Life Sciences, Shanghai University, Shanghai, 200444



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