Author

M. B. Schultz

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· 3 Physiology· 1

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Research Focus:differential expression· 2 Approach:experimental assay· 2 Assay:whole-genome sequencing· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

T. Seemann· 3 shared A. Goncalves da Silva· 2 shared B. P. Howden· 2 shared M. Sait· 2 shared N. L. Sherry· 2 shared S. A. Ballard· 2 shared T. P. Stinear· 2 shared A. Buultjens· 1 shared

Activity by Year

2017-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersMicrobiology

T. Seemann

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Author profile →

4 papers

Recent·Most cited

Bridging the gap between bioinformatics and the clinical and public health microbiology laboratory: An ISO-accredited genomics workflow for antimicrobial resistance

N. L. Sherry, K. Horan, S. A. Ballard, A. Goncalves da Silva, C. L. Gorrie, M. B. Schultz, K. Stevens, M. Valcanis, M. Sait, T. P. Stinear, B. P. Howden, T. Seemann

This paper develops and validates abritAMR, an ISO-accredited bioinformatics platform for detecting antimicrobial resistance genes with high accuracy and specificity.

bioRxiv · May 26, 2022 · PreprintMicrobiology

Validation of a Single-step, Single-tube Reverse Transcription-Loop-Mediated Isothermal Amplification Assay for Rapid Detection of SARS-CoV-2 RNA

J. Y. Lee, N. Best, J. L. McAuley, J. Porter, T. Seemann, M. B. Schultz, M. Sait, N. Orlando, K. Mercoulia, S. A. Ballard, J. Druce, T. Tran, M. Catton, M. J. Pryor, H. L. Cui, , T. P. Stinear

The paper validates a single-step, single-tube RT-LAMP assay for rapid SARS-CoV-2 detection with high specificity and sensitivity.

Journal of Medical Microbiology · Sep 1, 2020Peer-reviewedMicrobiology

Age and life expectancy clocks based on machine learning analysis of mouse frailty

M. B. Schultz, A. E. Kane, S. J. Mitchell, M. R. MacArthur, E. Warner, J. R. Mitchell, S. E. Howlett, M. S. Bonkowski, D. A. Sinclair

This paper develops two machine learning clocks to predict chronological age and life expectancy in mice using Frailty Indices, showing their accuracy in predicting lifespan and intervention efficacy.

bioRxiv · Dec 20, 2019Physiology

A Supervised Statistical Learning Approach For Accurate Legionella pneumophila Source Attribution During Outbreaks

A. Buultjens, K. Chua, S. Baines, J. Kwong, W. Gao, Z. Cutcher, S. Adcock, S. Ballard, M. B. Schultz, T. Tomita, N. Subasinghe, G. Carter, S. Pidot, L. Franklin, T. Seemann, , T. Stinear

This paper develops a statistical learning method to accurately attribute Legionella pneumophila outbreaks by analyzing genomic data and comparing it to epidemiological data.

bioRxiv · Aug 11, 2017Microbiology