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Chun-Hsi Huang
Researcher at University of Connecticut
Publications - 68
Citations - 1139
Chun-Hsi Huang is an academic researcher from University of Connecticut. The author has contributed to research in topics: Parallel algorithm & Sequence motif. The author has an hindex of 16, co-authored 68 publications receiving 1043 citations. Previous affiliations of Chun-Hsi Huang include University at Buffalo.
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Journal ArticleDOI
Biological network motif detection: principles and practice
TL;DR: The biological significance of network motifs, the motivation behind solving the motif-finding problem, and strategies to solve the various aspects of this problem are discussed.
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Minimotif Miner: a tool for investigating protein function.
Sudha Balla,Vishal Thapar,Snigdha Verma,ThaiBinh Luong,Tanaz Faghri,Chun-Hsi Huang,Sanguthevar Rajasekaran,Jacob J. del Campo,Jessica H Shinn,William A. Mohler,Mark W. Maciejewski,Michael R. Gryk,Bryan Piccirillo,Stanley R Schiller,Martin R. Schiller +14 more
TL;DR: A motif database comprising 312 unique motifs and a web-based tool for identifying motifs in proteins are constructed and functional motifs predicted by MnM are validated by analyzing thousands of confirmed examples and by confirming prediction of previously unidentified 14-3-3 motifsIn EFF-1.
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LASAGNA-Search: an integrated web tool for transcription factor binding site search and visualization.
Chih Lee,Chun-Hsi Huang +1 more
TL;DR: A novel integrated web tool named LASAGNA-Search that allows users to perform transcription factor binding site (TFBS) searches without leaving the web site, and uses the LAsAGNA (Length-Aware Site Alignment Guided by Nucleotide Association) algorithm for TFBS alignment.
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PCA-based population structure inference with generic clustering algorithms
TL;DR: This work proposes to apply PCA to the genotype data of a population, select the significant principal components using the Tracy-Widom distribution, and assign the individuals to one or more subpopulations using generic clustering algorithms.
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Exact algorithms for planted motif problems.
TL;DR: This paper presents algorithms for the planted (l, d)-motif problem that always find the correct answer(s) and is confident that the techniques introduced in this paper will find independent applications.