Author

A. Ghorbani

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 Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:ecology & biodiversity· 2 Approach:experimental assay· 2 Assay:metagenomics· 2 Research Focus:benchmarking· 1 Organism:chicken / avian· 1 Approach:deep learning· 1 Assay:imaging· 1 Organism:multiple species· 1

Frequent Co-Authors

M. KC· 2 shared T. J. Johnson· 2 shared A. Abid· 1 shared B. He· 1 shared B. P. Weber· 1 shared B. Weber· 1 shared C. W. Lee· 1 shared D. H. Liang· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersMicrobiology

M. KC

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Assessment of two DNA extraction kits for profiling poultry respiratory microbiota from multiple sample types

M. E. C. Abundo, J. Ngunjiri, K. J. M. Taylor, H. Ji, A. Ghorbani, M. KC, B. P. Weber, T. J. Johnson, L. Chang-Won

The paper compares two DNA extraction kits for poultry respiratory microbiota profiling, finding they yield similar microbial community results despite differences in DNA concentration and 16S rRNA gene copy numbers.

bioRxiv · Oct 21, 2020Microbiology

Respiratory and gut microbiota in commercial turkey flocks with disparate weight gain trajectories display differential compositional dynamics

K. J. Taylor, J. M. Ngunjiri, M. Abundo, H. Jang, M. Elaish, A. Ghorbani, M. KC, B. Weber, T. J. Johnson, C. W. Lee

This study compares respiratory and gut microbiota in two turkey flocks with different weight gain trajectories, finding that microbial composition changes over time and correlates with weight gain differences.

bioRxiv · Feb 19, 2020Microbiology

Deep Learning Interpretation of Echocardiograms

A. Ghorbani, D. Ouyang, A. Abid, B. He, J. H. Chen, R. A. Harrington, D. H. Liang, E. A. Ashley, J. Y. Zou

This paper applies deep learning to echocardiograms to identify cardiac structures, estimate function, and predict systemic phenotypes, showing high accuracy in key metrics.

bioRxiv · Jun 24, 2019Bioengineering