Author

M. T. Angulo

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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Ecology· 4 Microbiology· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 5 Assay:metagenomics· 4 Research Focus:ecology & biodiversity· 3 Research Focus:benchmarking· 2 Research Focus:cell type annotation· 2 Approach:deep learning· 2 Organism:human· 2 Organism:mouse· 2

Frequent Co-Authors

Y.-Y. Liu· 4 shared C. H. Moog· 2 shared S. Saavedra· 2 shared S. T. Weiss· 2 shared A. Aparicio· 1 shared A. Kelley· 1 shared C. Song· 1 shared H. Flores-Arguedas· 1 shared

Activity by Year

2017-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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4 shared papersMicrobiology

Y.-Y. Liu

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Author profile →

6 papers

Recent·Most cited

Assembly archetypes in ecological communities

H. Flores-Arguedas, O. Antolin-Camarena, S. Saavedra, M. T. Angulo

The paper identifies assembly archetypes in ecological communities using mathematical formalism and clinical data, distinguishing healthy from unhealthy gut microbiota.

bioRxiv · Jun 20, 2023Ecology

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

Coexistence holes characterize the assembly and disassembly of multispecies systems

M. T. Angulo, A. Kelley, L. Montejano, C. Song, S. Saavedra

The paper introduces a hypergraph-based formalism to analyze coexistence in multispecies systems, revealing predictable discontinuities called coexistence holes.

bioRxiv · Oct 16, 2020Ecology

Identifying sensor species to predict critical transitions in complex ecosystems

A. Aparicio, J. X. Velasco, C. H. Moog, Y.-Y. Liu, M. T. Angulo

The paper develops a method to identify sensor species in ecosystems to predict critical transitions using network structure, validated on experimental lake data.

bioRxiv · Nov 19, 2018 · PreprintEcology

Controlling complex microbial communities: a network-based approach

M. T. Angulo, C. H. Moog, Y.-Y. Liu

This paper develops a network-based framework to control microbial communities by identifying driver species, validated on gut and sponge microbiota.

bioRxiv · Nov 19, 2018 · PreprintMicrobiology

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