Author

S. T. Weiss

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 7 Biophysics· 1 Ecology· 1

Signature Assays, Organisms & Methods

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Organism:human· 7 Assay:metagenomics· 6 Approach:deep learning· 5 Research Focus:cell type annotation· 4 Research Focus:benchmarking· 3 Research Focus:cell-cell communication· 3 Research Focus:gene regulatory network inference· 3 Approach:statistical model· 3

Frequent Co-Authors

Y.-Y. Liu· 7 shared T. Wang· 3 shared X.-W. Wang· 3 shared A. A. Litonjua· 2 shared F. B. Hu· 2 shared J. Friedman· 2 shared M. K. Waldor· 2 shared M. T. Angulo· 2 shared

Activity by Year

2017-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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7 shared papersMicrobiology

Y.-Y. Liu

Mutual co-authors in cluster

Author profile →

9 papers

Recent·Most cited

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments

T. Wang, Y. Fu, M. Shuai, J.-S. Zheng, L. Zhu, A. T. Chan, Q. Sun, F. B. Hu, S. T. Weiss, Y.-Y. Liu

This study introduces METRIC, a deep-learning method that uses gut microbiome data to correct random errors in self-reported dietary assessments, showing effectiveness in simulated and real-world datasets.

bioRxiv · Nov 21, 2023Microbiology

Identifying keystone species in microbial communities using deep learning

X.-W. Wang, Z. Sun, H. Jia, S. Michel-Mata, M. T. Angulo, L. Dai, X. He, S. T. Weiss, Y.-Y. Liu

This paper introduces a deep learning framework to identify keystone species in microbial communities using synthetic and real microbiome data, finding taxa with high keystoneness show community specificity.

bioRxiv · Mar 15, 2023Microbiology

Predicting metabolite response to dietary intervention using deep learning

T. Wang, H. D. Holscher, S. Maslov, F. B. Hu, S. T. Weiss, Y.-Y. Liu

This paper develops a deep learning model to predict individual metabolite responses to dietary interventions based on gut microbiota composition, showing improved performance over traditional methods.

bioRxiv · Mar 14, 2023Microbiology

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

T. Wang, X.-W. Wang, K. Lee-Sarwar, A. A. Litonjua, S. T. Weiss, Y. Sun, S. Maslov, Y.-Y. Liu

This paper introduces mNODE, a neural ODE-based method predicting metabolomic profiles from microbial composition, showing superior performance and interpretability across human and environmental microbiomes.

Nature Machine Intelligence · Mar 13, 2023Peer-reviewedMicrobiology

Dissecting the Role of the Human Microbiome in COVID-19 via Metagenome-assembled Genomes

S. Ke, S. T. Weiss, Y.-Y. Liu

This study uses metagenome-assembled genomes to analyze the human microbiome's role in COVID-19, identifying gut microbiome signatures that distinguish cases from controls and predict disease progression.

bioRxiv · Mar 9, 2022Microbiology

Deciphering Functional Redundancy in the Human Microbiome

L. Tian, X.-W. Wang, A.-K. Wu, Y. Fan, J. Friedman, A. Dahlin, M. K. Waldor, G. Weinstock, S. T. Weiss, Y.-Y. Liu

The paper investigates functional redundancy in the human microbiome by analyzing genomic content networks and developing a genome evolution model to explain their topological features.

Nature Communications · Dec 4, 2020Peer-reviewedMicrobiology

Epithelial layer unjamming shifts energy metabolism toward glycolysis

S. J. DeCamp, V. M. K. Tsuda, J. Ferruzzi, S. A. Koehler, D. Roblyer, J. T. Giblin, M. H. Zaman, S. T. Weiss, M. DeMarzio, C. Y. Park, N. C. Ogassavara, J. A. Mitchel, J. P. Butler, J. J. Fredberg

The paper shows that during epithelial layer unjamming, cells shift their metabolism toward glycolysis, even in regions without visible morphological changes.

bioRxiv · Jun 30, 2020Biophysics

Effects of exclusive breastfeeding on infant gut microbiota: a meta-analysis across studies and populations

N. T. Ho, F. Li, K. A. Lee-Sarwar, H. M. Tun, B. Brown, P. S. Pannaraj, J. M. Bender, M. B. Azad, A. L. Thompson, S. T. Weiss, M. A. Azcarate-Peril, A. A. Litonjua, A. L. Kozyrskyj, H. B. Jaspan, G. M. Aldrovandi, L. Kuhn

This study meta-analyzes seven studies to show that exclusive breastfeeding increases gut microbiota diversity and specific metabolic pathways while reducing others, with benefits persisting beyond six months.

bioRxiv · Apr 4, 2018Microbiology

Mapping the ecological networks of microbial communities

Y. Xiao, M. T. Angulo, J. Friedman, M. K. Waldor, S. T. Weiss, Y.-Y. Liu

This paper introduces a method to infer microbial ecological networks from steady-state data without assuming population dynamics models, validated on simulated and experimental datasets.

bioRxiv · Oct 26, 2017Ecology