Abstract
MicroRNAs are a class of small RNA molecules, which play an important regulatory role for the gene expression of animals and plants. Various studies have proved that microRNAs tend to cluster on chromosomes. In this regard, a novel clustering algorithm is proposed in this paper, integrating rough hypercuboid approach and interval type-2 fuzzy c-means. Rough hypercuboid equivalence partition matrix is used here to compute the lower approximation and boundary region implicitly for the clusters without the need of any user-specified threshold. Interval-valued fuzzifier is used to deal with the uncertainty associated with the fuzzy clustering parameters. The effectiveness of proposed method, along with a comparison with existing clustering techniques, is demonstrated on several microRNA data sets using some widely used cluster validity indices.
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Garai, P., Maji, P. (2018). Identification of Co-expressed microRNAs Using Rough Hypercuboid-Based Interval Type-2 Fuzzy C-Means Algorithm. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_6
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DOI: https://doi.org/10.1007/978-981-10-6875-1_6
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