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

被引：8
作者：
Liang, Ruirui [1 ]
Xie, Jiayang [1 ]
Zhang, Chi [2 ]
Zhang, Mengying [1 ]
Huang, Hai [1 ]
Huo, Haizhong [3 ]
Cao, Xin [4 ]
Niu, Bing [1 ]
机构：
[1] Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China
[2] Foshan Huaxia Eye Hosp, Huaxia Eye Hosp Grp, Foshan 528000, Peoples R China
[3] Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Dept Gen Surg, Shanghai 200011, Peoples R China
[4] Fudan Univ, Zhongshan Hosp, Inst Clin Sci, Shanghai Med Coll, Shanghai 200032, Peoples R China
来源：
CURRENT TOPICS IN MEDICINAL CHEMISTRY | 2019年 / 19卷 / 25期
关键词：
Big data; Machine learning; Next generation sequencing; High-through sequence; Support vector machine; Naive Bayes classifier; Artifical neural work; Ensemble learning; Adaboost; bagging; DIFFERENTIAL EXPRESSION ANALYSIS; LYSINE SUCCINYLATION SITES; SEQUENCE-BASED PREDICTOR; CRITICAL SPHERICAL-SHELL; FLEXIBLE WEB SERVER; COILED-COIL DOMAINS; FEATURE-SELECTION; K-TUPLE; ENSEMBLE CLASSIFIER; STRUCTURAL BASIS;
D O I：
10.2174/1568026619666191016155543
中图分类号：
R914 [药物化学];
学科分类号：
100701 ;
摘要：
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 naive Bayes classifiers.
引用
收藏
页码：2301 / 2317
页数：17
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