Author

I. Amin

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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Plant Biology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:plant (other)· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

S. Mansoor· 2 shared A. A. Khan· 1 shared A. Amin· 1 shared A. Ben-Hur· 1 shared A. Faiq· 1 shared A. Hussain· 1 shared A. Nazar· 1 shared D. Grammatopoulos· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersPlant Biology

S. Mansoor

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Comparative Analysis, Diversification and Functional Validation of Plant Nucleotide-Binding Site Domain Genes

A. Hussain, A. A. Khan, M. Q. Aslam, A. Nazar, N. Zaman, A. Amin, M. A. Mahmood, M. S. Mukhtar, H. U. U. Rahman, M. Farooq, M. Saeed, I. Amin, S. Mansoor

This study identifies and classifies NBS domain genes across 34 plant species, revealing structural diversity and functional roles in plant-pathogen interactions.

bioRxiv · Aug 9, 2023Plant Biology

Fully Transient CRISPR/Cas12f system in plants capable of broad-spectrum resistance against Begomovirus

S. Haider, A. Faiq, M. Z. Khan, S. Mansoor, I. Amin

The paper develops a transient CRISPR/Cas12f system in plants that provides broad-spectrum resistance against Begomoviruses with high efficiency and reduced off-target effects.

bioRxiv · Jun 7, 2022 · PreprintPlant Biology

Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling

F. Minhas, D. Grammatopoulos, L. Young, I. Amin, D. Snead, N. Anderson, A. Ben-Hur, N. Rajpoot

This paper proposes agglomerative sampling to improve testing efficiency for low-prevalence COVID-19 populations, achieving ~40% test savings in simulations.

bioRxiv · Apr 13, 2020 · PreprintBioinformatics