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

1
College of Life Science, Shanghai University, Shanghai 200444, China
2
Department of Medical Informatics, Erasmus MC, 3015 CE Rotterdam, The Netherlands
3
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
4
Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai 200241, China
5
Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou 510507, China
6
Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200438, China
7
Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2019, 20(9), 2185; https://doi.org/10.3390/ijms20092185
Received: 24 February 2019 / Revised: 29 April 2019 / Accepted: 30 April 2019 / Published: 2 May 2019
(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)
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Abstract

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. View Full-Text
Keywords: snoRNA; cancer type; Monte Carlo feature selection; support vector machine; RIPPER algorithm snoRNA; cancer type; Monte Carlo feature selection; support vector machine; RIPPER algorithm
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Pan, X.; Chen, L.; Feng, K.-Y.; Hu, X.-H.; Zhang, Y.-H.; Kong, X.-Y.; Huang, T.; Cai, Y.-D. Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms. Int. J. Mol. Sci. 2019, 20, 2185.

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