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Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

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 methyl...

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Autores principales: Pan, Xiaoyong, Chen, Lei, Feng, Kai-Yan, Hu, Xiao-Hua, Zhang, Yu-Hang, Kong, Xiang-Yin, Huang, Tao, Cai, Yu-Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539089/
https://www.ncbi.nlm.nih.gov/pubmed/31052553
http://dx.doi.org/10.3390/ijms20092185
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author Pan, Xiaoyong
Chen, Lei
Feng, Kai-Yan
Hu, Xiao-Hua
Zhang, Yu-Hang
Kong, Xiang-Yin
Huang, Tao
Cai, Yu-Dong
author_facet Pan, Xiaoyong
Chen, Lei
Feng, Kai-Yan
Hu, Xiao-Hua
Zhang, Yu-Hang
Kong, Xiang-Yin
Huang, Tao
Cai, Yu-Dong
author_sort Pan, Xiaoyong
collection PubMed
description 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.
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spelling pubmed-65390892019-06-04 Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms Pan, Xiaoyong Chen, Lei Feng, Kai-Yan Hu, Xiao-Hua Zhang, Yu-Hang Kong, Xiang-Yin Huang, Tao Cai, Yu-Dong Int J Mol Sci Article 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. MDPI 2019-05-02 /pmc/articles/PMC6539089/ /pubmed/31052553 http://dx.doi.org/10.3390/ijms20092185 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pan, Xiaoyong
Chen, Lei
Feng, Kai-Yan
Hu, Xiao-Hua
Zhang, Yu-Hang
Kong, Xiang-Yin
Huang, Tao
Cai, Yu-Dong
Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
title Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
title_full Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
title_fullStr Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
title_full_unstemmed Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
title_short Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
title_sort analysis of expression pattern of snornas in different cancer types with machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539089/
https://www.ncbi.nlm.nih.gov/pubmed/31052553
http://dx.doi.org/10.3390/ijms20092185
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