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Rui Jiang

Researcher at Tsinghua University

Publications -  279
Citations -  7441

Rui Jiang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Gene. The author has an hindex of 34, co-authored 245 publications receiving 5736 citations. Previous affiliations of Rui Jiang include Chongqing University & Queensland University of Technology.

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The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Leming Shi, +201 more
- 01 Aug 2010 - 
TL;DR: P predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans are generated.
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Network-based global inference of human disease genes

TL;DR: A tool named CIPHER is developed to predict and prioritize disease genes, and it is shown that the global concordance between the human protein network and the phenotype network reliably predicts disease genes.
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Computational systems biology.

TL;DR: This survey of the bioinformatics analysis of NGS data can help researchers to choose appropriate tools when dealing with the sequencing data and introduce popular methods in quantitating the semantic similarity between ontology terms and their software implementations.
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A random forest approach to the detection of epistatic interactions in case-control studies

TL;DR: The gini importance offers yet another measure for the associations between SNPs and complex diseases, thereby complementing existing statistical measures to facilitate the identification of epistatic interactions and the understanding of epistasis in the pathogenesis of complex diseases.
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Clustering 16S rRNA for OTU prediction

TL;DR: An unsupervised Bayesian clustering method termed Clustering 16S rRNA for OTU Prediction (CROP) is proposed that can find clusters based on the natural organization of data without setting a hard cut-off threshold (3%/5%) as required by hierarchical clustering methods.