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Pattern Mining and Identifying Co-Expressed Genes from RNA-Seq Dataset Using a New Swarm Intelligence-Based Clustering

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RNA-Seq datasets represent genome-wide gene expression. RNA-Seq technique enable researchers to study transcriptome based on next-generation sequencing methods. Analysis of these kind of data are extremely dependent on Bioinformatics methods. There are always challenges for RNA-Seq analysis such as normalization, clustering, overlapping expression analysis and etc., which can adversely impact the RNA-Seq data analysis. Furthermore, there are a few method to cluster RNA-Seq specially when there are a lot of overlap reads. Swarm Intelligencebased learning is a trustworthy option to reach a strong partition of data. This article introduces a new clustering learning based on the swarm intelligence-clustering algorithm to cluster RNA-Seq data. Our method needs variability and swarm which is involved in randomness. In the proposed method, various runnings of ant colony clustering cause a number of diverse partitions. Considering these consequences as a new space datasets, we make a final clustering by a simple partitioning algorithm to gather them in a gratifying partition. Experimental consequences on some RNA-Seq datasets are shown to present the effectiveness of the proposed method in inducing the final partition and make the best cluster of RNA-Seqs.
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Keywords: BIOINFORMATICS; CLUSTERING; GENE EXPRESSION; PATTERN MINING; RNA-SEQ DATASET; SWARM INTELLIGENCE

Document Type: Research Article

Publication date: January 1, 2017

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  • Advanced Science, Engineering and Medicine (ASEM) is a science, engineering, technical and medical journal focused on the publishing of peer-reviewed multi-disciplinary research articles dealing with all fundamental and applied research aspects in the areas of (1) Physical Sciences, (2) Engineering, (3) Biological Sciences/Health Sciences, (4) Medicine, (5) Computer and Information Sciences, (6) Mathematical Sciences, (7) Agriculture Science and Engineering, (8) Geosciences, and (9) Energy/Fuels/Environmental/Green Science and Engineering.
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