Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
文献类型：期刊论文
| 作者 | Pan, Xiaoyong4,5; Cai, Yu-Dong4; Chen, Lei1,6; Feng, Kai-Yan7; Hu, Xiao-Hua3; Zhang, Yu-Hang2; Kong, Xiang-Yin2; Huang, Tao2; , |
| 刊名 | INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
 |
| 出版日期 | 2019 |
| 卷号 | 20期号:9页码:2185 |
| 关键词 | snoRNA cancer type Monte Carlo feature selection support vector machine RIPPER algorithm |
| ISSN号 | 1422-0067 |
| DOI | 10.3390/ijms20092185 |
| 文献子类 | Article |
| 英文摘要 | Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew's correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules. |
| 学科主题 | Biochemistry & Molecular Biology ; Chemistry |
| WOS关键词 | SMALL NUCLEOLAR RNAS ; CARLO FEATURE-SELECTION ; NONCODING RNA ; RIBOSOMAL-RNA ; LUNG-CANCER ; BOX C/D ; IDENTIFICATION ; HOST ; BINDING ; GENES |
| 语种 | 英语 |
| WOS记录号 | WOS:000469753500132 |
| 出版者 | MDPI |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/574]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.East China Normal Univ, Shanghai Key Lab PMMP, Shanghai 200241, Peoples R China; 2.Chinese Acad Sci, Shanghai Inst Nutr & Hlth, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China, 3.Fudan Univ, Sch Life Sci, Dept Biostat & Computat Biol, Shanghai 200438, Peoples R China; 4.Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China; 5.Erasmus MC, Dept Med Informat, NL-3015 CE Rotterdam, Netherlands; 6.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; 7.Guangdong AIB Polytech, Dept Comp Sci, Guangzhou 510507, Guangdong, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Pan, Xiaoyong,Cai, Yu-Dong,Chen, Lei,et al. Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2019,20(9):2185. |
| APA | Pan, Xiaoyong.,Cai, Yu-Dong.,Chen, Lei.,Feng, Kai-Yan.,Hu, Xiao-Hua.,...&,.(2019).Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,20(9),2185. |
| MLA | Pan, Xiaoyong,et al."Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 20.9(2019):2185. |
入库方式： OAI收割
来源：上海营养与健康研究所
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