Author

I. Friedberg

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· 2 Evolutionary Biology· 1 Plant Biology· 1

Signature Assays, Organisms & Methods

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Organism:bacteria (other)· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:chromatin & epigenetic regulation· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. D. Hildreth· 1 shared A. J. Gonzalez· 1 shared A. Jain· 1 shared A. Marcassa· 1 shared B. Ganapathysubramanian· 1 shared C. J. Lawrence-Dill· 1 shared C. M. Andorf· 1 shared D. A. Campbell· 1 shared

Activity by Year

2018-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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1 shared paperMicrobiology

A. D. Hildreth

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Unraveling a tangled skein: Evolutionary analysis of the bacterial gibberellin biosynthetic operon

R. S. Nett, N. N. Nguyen, R. Nagel, A. Marcassa, T. C. Charles, I. Friedberg, R. J. Peters

This study analyzes the evolutionary history of bacterial gibberellin biosynthetic operons, revealing complex horizontal gene transfer patterns and distinct genetic compositions across rhizobia and phytopathogens.

bioRxiv · Dec 8, 2019Microbiology

Tracing the Ancestry of Operons in Bacteria

H. N. Nguyen, A. Jain, O. Eulenstein, I. Friedberg

This paper develops a maximum parsimony algorithm to reconstruct ancestral operon states and introduces a software tool for exploring gene block and operon evolution.

bioRxiv · Jan 21, 2019Evolutionary Biology

Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

N. Zhou, Z. D. Siegel, S. Zarecor, N. Lee, D. A. Campbell, C. M. Andorf, D. Nettleton, C. J. Lawrence-Dill, B. Ganapathysubramanian, J. W. Kelly, I. Friedberg

This paper evaluates crowdsourcing for generating high-quality ground truth data for machine learning in plant phenomics, finding that Amazon MTurk workers match expert performance.

bioRxiv · May 30, 2018Plant Biology

Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains

F. R. Fields, S. D. Freed, K. E. Carothers, M. N. Hamid, D. E. Hammers, J. N. Ross, V. R. Kalwajtys, A. J. Gonzalez, A. D. Hildreth, I. Friedberg, S. W. Lee

This study develops a pipeline combining machine learning and biophysical filters to design and test minimal bacteriocin peptides for antimicrobial activity.

bioRxiv · May 4, 2018Microbiology