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Analysis of Next-Generation Sequencing data of miRNA for the Prediction of Breast Cancer

Recently, Next-Generation Sequencing (NGS) has emerged as revolutionised technique in the elds of `-omics' research. The Cancer Research Atlas (TCGA) is a great example of it where massive amount of sequencing data is present for miRNA and mRNA. Analysing these data could bring out some potential biological insight. Moreover, developing a prognostic system on this newly available sequencing data will give a greater help to cancer diagnosis. Hence, in this article, we have made an humble attempt to analyse such sequencing data of miRNA for accurate prediction of Breast Cancer. Generally miRNAs are small non-coding RNAs which are shown to participate in several carcinogenic processes either by tumor suppressors or oncogenes. This is the reason clinical treatment of the breast cancer patient has changed nowadays. Thus it is quite interesting to see the role of miRNAs for the prediction of breast cancer. In this regard, we have developed a technique using Gravitation Search Algorithm, which optimizes the underlying classi fication performance of Support Vector Machine. This proposed technique can select the potential feature, in this case miRNA, in order to achieve better prediction accuracy. In this study, we have achieved the classi fication accuracy upto 92% as well as found potential miRNAs. Top eight miRNAs are reported, which are hhsa-miR-10b, hsa-miR-107, hsa-miR- 10a, hsa-let-7a-1, hsa-let-7e, hsa-miR-101-2, hsa-let-7c and hsa-miR-100. The performance of the proposed technique is compared with seven other state-of-the-art techniques. Finally, the results have been justi ed by means of statistical test along with biological signi cance analysis of the selected miRNAs.


International Conference on Swarm, Evolutionary and Memetic Computing, Hyderabad, India , 2015

Autori IIT:

Indrajit Saha

Foto di Indrajit Saha

Tipo: Articolo in Atti di convegno internazionale con referee
Area di disciplina: Computer Science & Engineering

Attività: Biologia computazionale