Michael I. Love: h-index, Total Citations, and Citation Map
Michael I. Love's h-index is 43 (86 i10-index, 133,494+ total citations across 5+ publications) according to Google Scholar as of May 2026. Michael I. Love is affiliated with Associate Professor, Genetics and Biostatistics, UNC-Chapel Hill.
Michael I. Love is a researcher affiliated with Associate Professor, Genetics and Biostatistics, UNC-Chapel Hill, specializing in statistics, biostatistics, genomics. Their work has been cited 133,494 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Michael I. Love's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 43
- i10-Index
- 86
- Total Citations
- 133,494
- Citing Countries
- 18
As of May 2026.
Michael I. Love has an h-index of 43 and 133,494 total citations across 5 publications, with research cited by institutions in 18 countries.
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We've mapped 5,000 of 133,494 citations for Michael I. Love
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Top Cited Works
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
201499,055
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed DESeq2, a seminal statistical framework for moderated estimation of fold change and dispersion in RNA-seq data, establishing a standard for differential expression analysis.
The researcher developed a foundational framework for orchestrating high-throughput genomic analysis using Bioconductor, establishing a standard for reproducible computational biology workflows.
The researcher developed MAGeCK, a computational framework enabling robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens, establishing a standard for high-throughput genetic analysis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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