paper-with-me

Papers

SpliceCombo: A Hybrid Technique efficiently use for Principal Component Analysis of Splice Site Prediction

2019-07-19

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.

📄 PDF Abstract BibTeX arXiv:1907.09401

Code (0)

등록된 구현이 없습니다.

Tasks

feature selectionPredictionSensitivitySpecificitySplice Site Prediction

Similar Papers 제목 키워드 기반

Attn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion

2020-10-13 · Sunny Verma, Chen Wang, Liming Zhu, Wei Liu

The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in its convolution layers. Albeit powerful, the PCANet consists of basic operation…

Multi-Rank Sparse and Functional PCA: Manifold Optimization and Iterative Deflation Techniques

2019-07-28 · Michael Weylandt

We consider the problem of estimating multiple principal components using the recently-proposed Sparse and Functional Principal Components Analysis (SFPCA) estimator. We first propose an extension of SFPCA which estimate…

GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis

2024-01-28 · Florian Heinrichs

Data analysis often requires methods that are invariant with respect to specific transformations, such as rotations in case of images or shifts in case of images and time series. While principal component analysis (PCA) …

Dimensionality ReductionTime Series

A Decomposition Framework for Certifiably Optimal Orthogonal Sparse PCA

2026-03-01 · Difei Cheng, Qiao Hu arxiv

Sparse Principal Component Analysis (SPCA) is an important technique for high-dimensional data analysis, improving interpretability by imposing sparsity on principal components. However, existing methods often fail to si…

Distributionally Robust Fair Principal Components via Geodesic Descents

2022-02-07 · ICLR 2022 4 · Hieu Vu, Toan Tran, Man-Chung Yue, Viet Anh Nguyen

Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines. In consequential domains such as college admission, healthcare and credit approval, it is imper…

Dimensionality ReductionFairness