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Huixiao Hong

Researcher at Food and Drug Administration

Publications -  209
Citations -  13717

Huixiao Hong is an academic researcher from Food and Drug Administration. The author has contributed to research in topics: Computer science & Gene. The author has an hindex of 53, co-authored 191 publications receiving 11843 citations. Previous affiliations of Huixiao Hong include National Institutes of Health & ICF International.

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Journal ArticleDOI

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

Leming Shi, +136 more
- 01 Sep 2006 - 
TL;DR: This study describes the experimental design and probe mapping efforts behind the MicroArray Quality Control project and shows intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed.
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A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

Zhenqiang Su, +164 more
- 01 Sep 2014 - 
TL;DR: The complete SEQC data sets, comprising >100 billion reads, provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings, and measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling.
Journal ArticleDOI

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.
Journal ArticleDOI

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

TL;DR: RNA-seq outperforms microarray in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts, and classifiers to predict MOAs perform similarly when developed using data from either platform.
Journal ArticleDOI

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

TL;DR: The results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs and recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists.