μHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix
Sushmita Paul,Pradipta Maji +1 more
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TLDR
The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem and is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.Abstract:
The miRNAs, a class of short approximately 22‐nucleotide non‐coding RNAs, often act post‐transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples. In this regard, a new miRNA selection algorithm, called μHEM, is presented based on rough hypercuboid approach. It selects a set of miRNAs from a microarray data by maximizing both relevance and significance of the selected miRNAs. The degree of dependency of sample categories on miRNAs is defined, based on the concept of hypercuboid equivalence partition matrix, to measure both relevance and significance of miRNAs. The effectiveness of the new approach is demonstrated on six publicly available miRNA expression data sets using support vector machine. The.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. An important finding is that the μHEM algorithm achieves lowest B.632+ error rate of support vector machine with a reduced set of differentially expressed miRNAs on four expression data sets compare to some existing machine learning and statistical methods, while for other two data sets, the error rate of the μHEM algorithm is comparable with the existing techniques. The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem. The method is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.read more
Citations
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Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering.
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Multimodal Omics Data Integration Using Max Relevance--Max Significance Criterion
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References
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Book ChapterDOI
Rough Sets for Insilico Identification of Differentially Expressed miRNAs
Pradipta Maji,Sushmita Paul +1 more
TL;DR: This chapter presents a new approach for selecting miRNAs from microarray expression data that integrates the merit of rough set-based feature selection algorithm reported in Chap.
Proceedings ArticleDOI
Robust RFCM algorithm for identification of co-expressed miRNAs
Sushmita Paul,Pradipta Maji +1 more
TL;DR: The application of robust rough-fuzzy c-means (rRFCM) algorithm to discover co-expressed miRNA clusters is presented and the effectiveness of the rRFCM algorithm and different initialization methods, along with a comparison with other related methods, is demonstrated.
Proceedings ArticleDOI
Rough sets and support vector machine for selecting differentially expressed miRNAs
Sushmita Paul,Pradipta Maji +1 more
TL;DR: A rough set based feature selection algorithm to select miRNAs from expression data that can classify tissue samples into their respective category with minimal error rate is presented.
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