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

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ID: 80498
2019
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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.
Reference Key
liang2019identifyingcurrent Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liang, Ruirui;Xie, Jiayang;Zhang, Chi;Zhang, Mengying;Huang, Hai;Huo, Haizhong;Cao, Xin;Niu, Bing;
Journal Current topics in medicinal chemistry
Year 2019
DOI
10.2174/1568026619666191016155543
URL
Keywords
Machine learning support vector machine big data next generation sequencing ensemble learning adaboost artifical neural work high-through sequence naïve bayes classifier bagging.

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