Prediction of regulatory gene pairs using dynamic time warping and gene ontology

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107145

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107145

Title:	Prediction of regulatory gene pairs using dynamic time warping and gene ontology
Authors:	施國琛;Yang, Andy C.;Hsu, Hui-Huang;Lu, Ming-Da;Tseng, Vincent S.;Shih, Timothy K.
Contributors:	資訊電機學院資訊工程學系
Keywords:	Algorithms;Biosciences and Bioinformatics;COMPUTING JOURNALS;Computing Science, Applications and Software;Data Mining - methods;Databases, Genetic;Gene Expression Profiling - methods;Gene Expression Regulation - genetics;Gene Ontology;Genes, Regulator - genetics;HEALTHCARE AND LEISURE JOURNALS;Healthcare and Medical Engineering;Oligonucleotide Array Sequence Analysis - methods;Protein Interaction Mapping - methods;TECHNICAL JOURNALS
Date:	2014-01-01
Issue Date:	2026-04-23 13:57:56 (UTC+8)
Publisher:	Inderscience Enterprises Ltd.;Switzerland: Inderscience Publishers Ltd
Abstract:	摘要： Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods. 其他題名： ijdmb 出版者： Switzerland: Inderscience Publishers Ltd 出版日期： 2014-01-01 出處： International journal of data mining and bioinformatics, 2014-01, Vol.10 (2), p.121-145 版權： Copyright © 2014 Inderscience Enterprises Ltd. 識別號： ISSN: 1748-5673 識別號： EISSN: 1748-5681 識別號： DOI: 10.1504/IJDMB.2014.064010 識別號： PMID: 25796734
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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