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个人简介
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.
研究兴趣
论文共 336 篇作者统计合作学者相似作者
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Nature Communicationsno. 1 (2024): 1-13
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
Lea Ann Chen,Huilin Li,Drew Jones,Chan Wang,Dalin Li,Arielle Radin,Jessica Baylor, Lily Deng, Susan Cataldo, Tanima Kundu, Jing Shen, Ashley Bush,
INFLAMMATORY BOWEL DISEASES (2024): S69-S70
Soyoung Kwak,Mykhaylo Usyk,Dia Beggs,Heesun Choi, Dariush Ahdoot, Feng Wu, Lorraine Maceda,Huilin Li, Eun-Ok Im,Hae-Ra Han, Eunjung Lee, Anna H. Wu,
The EMBO journalno. 8 (2024): 1634-1652
Nature Communicationsno. 1 (2024): 1-16
Lea Ann Chen,Huilin Li,Drew Jones,Chan Wang,Dalin Li,Arielle Radin,Jessica Baylor, Lily Deng, Susan Cataldo, Tanima Kundu, Jing Shen, Ashley Bush,
Gastroenterologyno. 3 (2024): S95-S96
J.G. Natalini, K.K. Wong, N.C. Nelson,B.G. Wu,D. Rudym,M.B. Lesko, S. Qayum,T.C. Lewis,S.H. Chang, J.C. Chan,T.C. Geraci, F.A. Tiripicchio,
Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundationno. 1 (2023): 35-44
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