title: Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles.
abstract: Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data. In this article, we propose a feature subset-based ensemble method in which each model is learned from a different projection of the original feature space to classify multiple cancers. In our method, the feature relevance and redundancy are considered to generate multiple feature subsets, the base classifiers are learned from each independent miRNA subset, and the average posterior probability is used to combine the base classifiers. To test the performance of our method, we used bead-based and sequence-based miRNA expression datasets and conducted 10-fold and leave-one-out cross validations. The experimental results show that the proposed method yields good results and has higher prediction accuracy than popular ensemble methods. The Java program and source code of the proposed method and the datasets in the experiments are freely available at https://sourceforge.net/projects/mirna-ensemble/.
authors.affiliation: Database/Bioinformatics Laboratory, College of Electrical & Computer Engineering, Chungbuk National University, Cheongju, 28644, South Korea. Electronic address: pyz@dblab.chungbuk.ac.kr.
authors.forename: Yongjun
authors.initials: Y
authors.lastname: Piao
authors.affiliation: Department of Computer Engineering, Dongguk University Gyoengju Campus, 38066, South Korea. Electronic address: myunghopark@gmail.com.
authors.forename: Minghao
authors.initials: M
authors.lastname: Piao
authors.affiliation: Database/Bioinformatics Laboratory, College of Electrical & Computer Engineering, Chungbuk National University, Cheongju, 28644, South Korea. Electronic address: khryu@dblab.chungbuk.ac.kr.
authors.forename: Keun Ho
authors.initials: KH
authors.lastname: Ryu
mesh_terms: D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D003663:Decision Trees; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D035683:MicroRNAs / Q000032:analysis* / Q000235:genetics / Q000378:metabolism; D009369:Neoplasms / Q000145:classification* / Q000235:genetics* / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs
keywords: Cancer classification; Data mining; Ensemble learning; miRNA expression