Rough sets for in silico identification of differentially expressed miRNAs
Sushmita Paul, Pradipta Maji Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India Abstract: The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or pro...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Dove Medical Press
2013
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| Acceso en línea: | https://doaj.org/article/d0554582e7374d6fbf1cc12a504a0072 |
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| Sumario: | Sushmita Paul, Pradipta Maji Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India Abstract: The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine. Keywords: microRNA, feature selection, bootstrap error, support vector machine |
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