SpliceCombo: A Hybrid Technique Efficiently Use for Principal Component Analysis of Splice Site Prediction

Abstract

The primary step in search of the gene prediction is an identification of the coding region from genomic DNA sequence. Gene structure in the case of a eukaryotic organism is composed of promoter, intron, start codon, exons, stop codon, etc. Splice site prediction, which separates the junction between exon and intron, though the sequence beside.The splice sites have huge preservation, however, the precision of the tool exhibits less than 90%. The main objective of this work to exhibits a hybrid technique that efficiently improves the existing gene recognition technique. Therefore to enhance the identification of splice sites, the respective algorithm needs to be improved. Over the last decade, the researcher paid more attention to improve the accuracy of a predicted model in this domain. Our proposed method, 'SpliceCombo' involves three stages. At initial stage, which considers the principal Component Analysis, based on the feature extracted. In the intermediate stage, i.e.,, the second stage Case-Based Reasoning is done, i.e., feature selection. The third stage uses support vector machine based along with polynomial kernel function for final classification. In comparison with other methods, the proposed SpliceCombo model outperforms other prediction models with respect to prediction accuracies. Particularly for donor splice site the methodology exhibits sensitivity is 97.25% accurate and specificity is 97.46% accurate. For acceptor Splice Site the sensitivity is 96.51% and Specificity is 94.48% correct.

Drought Assessment Based on Data Fusion and Deep Learning

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Design and Realization of a Fuzzy Comprehensive Evaluation System for Music Teaching in Higher Education

“…In China, existing works concerning ET quality and effect evaluation are mostly theoretical discussions, most of the field scholars focused on aspects such as the content and structure of ET effect evaluation indicators, and they built various evaluation systems [5]. Principal component analysis (PCA), analytic hierarchy process (AHP), BP neural network, and fuzzy comprehensive evaluation (FCE) are common methods adopted in ET effect evaluation in the Chinese academic circle [6][7][8][9][10][11][12]. Among the four, FCE is a comprehensive evaluation method established based on fuzzy mathematics, it can effectively solve fuzzy decision-making questions with multiple factors that are hard to quantify; since it can convert qualitative evaluation into quantitative evaluation, the method is very suitable for solving various non-deterministic problems [13].…”