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Bio
My Research
KEYWORDS
Biostatistics, Metagenomics/Microbiomics Data Analysis, Statistical Genomic Methodology, Survey methodology, Study design
SUMMARY
My central research focus is to develop and apply innovative statistical methods to solve challenging data analysis problems in three biomedical research areas: 1) high-throughput metagenomics/microbiome/genetics data analysis, 2) complex survey methodology, and 3) biomedical collaborative research.
I am the director of Metagenomic/Microbiomic Data Analysis Group in Department of Population health. With NIH’s support, my research group has been focused on developing novel statistical methods in analyzing metagenomics and microbiome data, including microbiome association test, longitudinal microbiome data analysis, and microbiome causal/mediation modeling. We work closely with various NYULMC research labs to ensure that the latest statistical methods are incorporated for optimal experimental design and downstream data analysis. We develop best practice data analysis pipelines for a variety of experimental designs that integrate proprietary software from the existing microbiome data analysis platform and the best Open Source tools, as well as software developed within our group.
KEYWORDS
Biostatistics, Metagenomics/Microbiomics Data Analysis, Statistical Genomic Methodology, Survey methodology, Study design
SUMMARY
My central research focus is to develop and apply innovative statistical methods to solve challenging data analysis problems in three biomedical research areas: 1) high-throughput metagenomics/microbiome/genetics data analysis, 2) complex survey methodology, and 3) biomedical collaborative research.
I am the director of Metagenomic/Microbiomic Data Analysis Group in Department of Population health. With NIH’s support, my research group has been focused on developing novel statistical methods in analyzing metagenomics and microbiome data, including microbiome association test, longitudinal microbiome data analysis, and microbiome causal/mediation modeling. We work closely with various NYULMC research labs to ensure that the latest statistical methods are incorporated for optimal experimental design and downstream data analysis. We develop best practice data analysis pipelines for a variety of experimental designs that integrate proprietary software from the existing microbiome data analysis platform and the best Open Source tools, as well as software developed within our group.
Research Interests
Papers共 389 篇Author StatisticsCo-AuthorSimilar Experts
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Nature Communicationsno. 1 (2024): 1-13
Lauren T. Berube,Collin J. Popp,Margaret Curran, Lu Hu,Mary Lou Pompeii,Souptik Barua, Emma Bernstein, Vanessa Salcedo,Huilin Li,David E. St-Jules,Eran Segal,Michael Bergman,
Trialsno. 1 (2024): 1-14
Sub-cellular biochemistry (2024): 383-408
bioRxiv : the preprint server for biology (2024)
Lu Hu,Yun Shi,Judith Wylie-Rosett,Mary Ann Sevick,Xinyi Xu, Ricki Lieu,Chan Wang,Huilin Li, Han Bao,Yulin Jiang, Ziqiang Zhu,Ming-Chin Yeh,
PLOS ONEno. 3 (2024): e0299799-e0299799
JOURNAL OF MOLECULAR STRUCTURE (2024): 137336
H. Diessel Duan,Huilin Li
Journal of Biological Chemistryno. 6 (2024): 107387-107387
INTERNATIONAL JOURNAL OF CANCERno. 4 (2024): 723-737
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