Author

M. E. Smith

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· 1 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:chromatin & epigenetic regulation· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

A. Eigege· 1 shared A. Lipzen· 1 shared A. Salamov· 1 shared A. Tritt· 1 shared B. K. Singh· 1 shared D. Carter-House· 1 shared E. Griswold· 1 shared E. Michael· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperEcology

A. Eigege

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2 papers

Recent·Most cited

Divergent evolution of early terrestrial fungi reveals the evolution of Mucormycosis pathogenicity factors

Y. Wang, Y. Chang, J. Ortanez, J. F. Pena, D. Carter-House, N. K. Reynolds, M. E. Smith, G. Benny, S. J. Mondo, A. Salamov, A. Lipzen, J. Pangilinan, J. Guo, K. LaButti, W. Andreopolous, , J. E. Stajich

This study investigates the divergent evolution of zygomycete fungi, identifying lineage-specific genomic elements and evolutionary relationships that contribute to Mucormycosis pathogenicity.

bioRxiv · Jun 24, 2022Evolutionary Biology

Predicting lymphatic filariasis elimination in data-limited settings: a reconstructive computational framework for combining data generation and model discovery

M. E. Smith, E. Griswold, B. K. Singh, E. Miri, A. Eigege, S. Adelamo, J. Umaru, K. Nwodu, Y. Sambo, J. Kadimbo, J. Danyobi, F. O. Richards, E. Michael

This paper develops a computational framework that combines data generation and model discovery to predict lymphatic filariasis elimination in data-limited settings, showing it can identify local transmission models and forecast intervention impacts.

PLOS Computational Biology · Jul 21, 2020Peer-reviewedEcology