Browsing by Author "Hsieh, Wan J."

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Human microRNA target identification by RRSM
(2011-10-07)
Hsieh, Wan J.
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Wang, Hsiuying
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統計學研究所
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Institute of Statistics
MicroRNAs (miRNAs) are small endogenously expressed non-coding RNAs that regulate target messenger RNAs in various biological processes. In recent years, there have been many studies concentrated on the discovery of new miRNAs and identification of their mRNA targets. Although researchers have identified many miRNAs, few miRNA targets have been identified by actual experimental methods. To expedite the identification of miRNA targets for experimental verification, in the literature approaches based on the sequence or microarray expression analysis have been established to discover the potential miRNA targets. In this study, we focus on the human miRNA target prediction and propose a generalized relative R(2) method (RRSM) to find many high-confidence targets. Many targets have been confirmed from previous studies. The targets for several miRNAs discovered by the HITS-CLIP method in a recent study have also been selected by our study. (C) 2011 Elsevier Ltd. All rights reserved.
Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling
(2014-04-22)
Hsieh, Wan J.
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Lin, Feng-Mao
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Huang, Hsien-Da
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Wang, Hsiuying
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生物資訊及系統生物研究所
Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.
Robust mixture modeling using the skew t distribution
(2007-06-01)
Lin, Tsung I.
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Lee, Jack C.
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Hsieh, Wan J.
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統計學研究所
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資訊管理與財務金融系註：原資管所+財金所
A finite mixture model using the Student's t distribution has been recognized as a robust extension of normal mixtures. Recently, a mixture of skew normal distributions has been found to be effective in the treatment of heterogeneous data involving asymmetric behaviors across subclasses. In this article, we propose a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings. Statistical mixture modeling based on normal, Student's t and skew normal distributions can be viewed as special cases of the skew t mixture model. We present analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates. The proposed methodology is illustrated by analyzing a real data example.
RRSM with a data-dependent threshold for miRNA target prediction
(2013-11-21)
Hsieh, Wan J.
;
Wang, Hsiuying
;
統計學研究所
;
Institute of Statistics
Predicting miRNA target genes is one of the important issues in bioinformatics. The correlation analysis is a widely used method for exploring miRNA targets through microarray data. However, the experimental results show that correlation analysis leads to large false positive or negative results. In addition, the correlation analysis is not appropriate when multiple miRNAs simultaneously regulate a gene. Recently, the relative R squared method (RRSM) has been proposed for miRNA target prediction, which is shown to be superior to some existing methods. To adopt the RRSM, we need first to set thresholds to select a proportion of potential targets. In the previous studies, the threshold is set to be fixed, which does not depend on the characteristic of a gene. Due to the diversity of the functions of genes, a data-dependent threshold may be more feasible in real data applications than a data-independent threshold. In this study, we propose a threshold selection method which is based on the distribution of the relative R squared statistic. The proposed method is shown to significantly improve the previous prediction results by selecting more experimentally validated targets. (C) 2013 Elsevier Ltd. All rights reserved.