C
Chen-Hsiang Yeang
Researcher at Academia Sinica
Publications - 66
Citations - 3904
Chen-Hsiang Yeang is an academic researcher from Academia Sinica. The author has contributed to research in topics: Gene & Regulation of gene expression. The author has an hindex of 18, co-authored 61 publications receiving 3715 citations. Previous affiliations of Chen-Hsiang Yeang include Institute for Advanced Study & University of California, Santa Cruz.
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Journal ArticleDOI
Multiclass cancer diagnosis using tumor gene expression signatures
Sridhar Ramaswamy,Pablo Tamayo,Ryan Rifkin,Sayan Mukherjee,Chen-Hsiang Yeang,Michael Angelo,Christine Ladd,Michael Reich,Eva Latulippe,Jill P. Mesirov,Tomaso Poggio,William L. Gerald,Massimo Loda,Eric S. Lander,Todd R. Golub,Todd R. Golub +15 more
TL;DR: The results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.
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Molecular classification of multiple tumor types.
Chen-Hsiang Yeang,Sridhar Ramaswamy,Pablo Tamayo,Sayan Mukherjee,Ryan Rifkin,Michael Angelo,Michael Reich,Eric S. Lander,Jill P. Mesirov,Todd R. Golub +9 more
TL;DR: This work obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples, and performed multi-class classification by combining the outputs of binary classifiers.
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Combinatorial patterns of somatic gene mutations in cancer
TL;DR: The observed mutational patterns suggest candidates of new cosequencing targets that can either reveal novel patterns or validate the predictions deduced from existing patterns, and provide guiding information for the ongoing cancer genome projects.
Journal ArticleDOI
Physical network models.
TL;DR: A new framework for inferring models of transcriptional regulation, which is based on annotated molecular interaction graphs, is developed and successfully predicts gene knock-out effects with a high degree of accuracy in a cross-validation setting.
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Detecting coevolution in and among protein domains.
Chen-Hsiang Yeang,David Haussler +1 more
TL;DR: An augmented continuous-time Markov process model for sequence coevolution is proposed that can handle different types of interactions, incorporate phylogenetic information and sequence substitution, has only one extra free parameter, and requires no knowledge about interaction rules.