Author

Shira Lynn Broschat

Washington State UniversityORCID 0000-0001-9894-7371Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Organism:bacteria (other)· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Kelly A. BraytoniD· 2 shared Jeff A. DailyiD· 1 shared Svetlana LockwoodiD· 1 shared Zhila Esna AshariiD· 1 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 4 mutual co-author links

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2 shared papersBioinformatics

Kelly A. BraytonORCID

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2 papers

Recent·Most cited

BioinformaticsPLoS ONEPeer-reviewedJan 25, 2019

Using an Optimal Set of Features with a Machine Learning-Based Approach to Predict Effector Proteins for Legionella pneumophila

Zhila Esna Ashari, Kelly A. Brayton, Shira Lynn Broschat

The paper uses an optimal set of 370 features with machine learning classifiers to predict effector proteins in Legionella pneumophila, achieving higher accuracy and identifying more validated effectors than previous studies.

15 citations

bacteria (other)proteomics

𝕏 PostBluesky

GenomicsbioRxivPreprintJun 21, 2018

Whole Proteome Clustering of 2,307 Genomes Reveals Remarkable Conservation of Four Proteins Among Proteobacteria While Revealing Significant Annotation Issues

Svetlana Lockwood, Kelly A. Brayton, Jeff A. Daily, Shira Lynn Broschat

The paper clusters 2,307 Proteobacterial genomes to identify conserved proteins and highlights annotation issues in proteome data.

1 citation

bacteria (other)protein sequence

𝕏 PostBluesky