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HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity.

Frontiers in genetics

Jiang S, Xiao G, Koh AY, Chen Y, Yao B, Li Q, Zhan X.
PMID: 32582274
Front Genet. 2020 Jun 03;11:445. doi: 10.3389/fgene.2020.00445. eCollection 2020.

The human microbiome is a collection of microorganisms. They form complex communities and collectively affect host health. Recently, the advances in next-generation sequencing technology enable the high-throughput profiling of the human microbiome. This calls for a statistical model to...

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Jiang S, Xiao G, Koh AY, et al. HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Front Genet. 2020;11:445doi: 10.3389/fgene.2020.00445.
Jiang, S., Xiao, G., Koh, A. Y., Chen, Y., Yao, B., Li, Q., & Zhan, X. (2020). HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Frontiers in genetics, 11445. https://doi.org/10.3389/fgene.2020.00445
Jiang, Shuang, et al. "HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity." Frontiers in genetics vol. 11 (2020): 445. doi: https://doi.org/10.3389/fgene.2020.00445
Jiang S, Xiao G, Koh AY, Chen Y, Yao B, Li Q, Zhan X. HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity. Front Genet. 2020 Jun 03;11:445. doi: 10.3389/fgene.2020.00445. eCollection 2020. PMID: 32582274; PMCID: PMC7283552.
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Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder.

Psychological medicine

Jang SK, Saunders G, Liu M, Jiang Y, Liu DJ, Vrieze S.
PMID: 32762793
Psychol Med. 2020 Aug 07;1-11. doi: 10.1017/S003329172000272X. Epub 2020 Aug 07.

BACKGROUND: Substance use occurs at a high rate in persons with a psychiatric disorder. Genetically informative studies have the potential to elucidate the etiology of these phenomena. Recent developments in genome-wide association studies (GWAS) allow new avenues of investigation.METHOD:...

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Jang SK, Saunders G, Liu M. Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder. Psychol Med. 2020;1-11doi: 10.1017/S003329172000272X.
Jang, S. K., Saunders, G., Liu, M., Jiang, Y., Liu, D. J., & Vrieze, S. (2020). Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder. Psychological medicine, 1-11. https://doi.org/10.1017/S003329172000272X
Jang, Seon-Kyeong, et al. "Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder." Psychological medicine vol. (2020): 1-11. doi: https://doi.org/10.1017/S003329172000272X
Jang SK, Saunders G, Liu M, Jiang Y, Liu DJ, Vrieze S. Genetic correlation, pleiotropy, and causal associations between substance use and psychiatric disorder. Psychol Med. 2020 Aug 07;1-11. doi: 10.1017/S003329172000272X. Epub 2020 Aug 07. PMID: 32762793; PMCID: PMC8759148.
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BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies.

medRxiv : the preprint server for health sciences

Jiang S, Zhou Q, Zhan X, Li Q.
PMID: 33469604
medRxiv. 2021 Jan 18; doi: 10.1101/2020.10.06.20208132.

The coronavirus disease of 2019 (COVID-19) is a pandemic. To characterize its disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model builds on a Bayesian Poisson segmented regression model that can...

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Jiang S, Zhou Q, Zhan X, et al. BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies. medRxiv. 2021;doi: 10.1101/2020.10.06.20208132.
Jiang, S., Zhou, Q., Zhan, X., & Li, Q. (2021). BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.10.06.20208132
Jiang, Shuang, et al. "BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies." medRxiv : the preprint server for health sciences vol. (2021). doi: https://doi.org/10.1101/2020.10.06.20208132
Jiang S, Zhou Q, Zhan X, Li Q. BayesSMILES: Bayesian Segmentation Modeling for Longitudinal Epidemiological Studies. medRxiv. 2021 Jan 18; doi: 10.1101/2020.10.06.20208132. PMID: 33469604; PMCID: PMC7814850.
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Showing 1 to 3 of 3 entries