Author

Michael I Love

University of North Carolina at Chapel Hill

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Same name, identified by ORCID

Biology Disciplines

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Bioinformatics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Approach:deep learning· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Research Focus:polygenic risk· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Rafael A Irizarry· 2 shared Abhinav NelloreiD· 1 shared Alisa Knodle ManningiD· 1 shared Alison Motsinger-Reif· 1 shared Alyssa C Frazee· 1 shared Andrew E Jaffe· 1 shared Ben LangmeadiD· 1 shared Christopher WilksiD· 1 shared

Activity by Year

2015-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersBioinformatics

Rafael A Irizarry

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Genetics in Context: Social Factors Modify Polygenic Risk Across Multiple Cardiometabolic Diseases

Micah R. Hysong, Sarah C Hsu, Farida S. Akhtari, Miriam S. Udler, Josep Maria Mercader, Alisa Knodle Manning, Michael D. Green, Iain R. Konigsberg, Luciana B. Vargas, Leslie A. Lange, Michael I Love, Megan M. Shuey, LáShauntá M. Glover, Alison Motsinger-Reif, Genevieve L. Wojcik, , Laura M. Raffield

The paper examines how social determinants of health modify polygenic risk scores for cardiometabolic diseases and shows that integrating social and genetic factors improves disease prediction.

medRxiv · Oct 29, 2026 · PreprintGenetic & Genomic Medicine

Flexible expressed region analysis for RNA-seq with derfinder

Leonardo Collado Torres, Abhinav Nellore, Alyssa C Frazee, Christopher Wilks, Michael I Love, Ben Langmead, Rafael A Irizarry, Jeffrey Leek, Andrew E Jaffe

The paper introduces derfinder, a tool for RNA-seq analysis that identifies differentially expressed regions at single base resolution without relying on gene annotations, showing it works well with incomplete annotations and large datasets.

Nucleic Acids Research · Sep 29, 2016Peer-reviewedBioinformatics

Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

Michael I Love, John B Hogenesch, Rafael A Irizarry

The paper introduces a method to correct RNA-seq transcript abundance estimates by modeling fragment sequence bias, reducing false positives related to alternative isoform usage.

bioRxiv · Aug 29, 2015Bioinformatics