Michael Love, PhD

About

Dr. Michael Love is an associate professor in the Department of Biostatistics and Department of Genetics at UNC. He earned his doctoral degree in computational biology in 2013 from the Freie Universität and Max Planck Institute for Molecular Genetics in Berlin.

His research concerns statistical and computational methods for the analysis of high-throughput sequencing assays to facilitate biomedical and biological research. He has developed a number of open source software packages for the analysis of RNA sequencing (RNA-seq) data, including the DESeq2 package for differential gene expression analysis. In addition, he studies the effect of lab-to-lab variation on computational estimation of gene isoform abundances from RNA-seq, and has developed statistical methods for accurate estimation of isoform abundance in the presence of common technical biases.

The Love Lab uses statistical models to infer biologically meaningful patterns in high-dimensional datasets, and develops open-source statistical software for the Bioconductor Project. At UNC-Chapel Hill, we often collaborate with groups in the Genetics Department and the Lineberger Comprehensive Cancer Center, studying how genetic variants relevant to diseases are associated with changes in molecular and cellular phenotypes.

Michael Love in the Gillings News

Honors and Awards

Recruitment Award
2019, UNC Center for Environmental Health and Susceptibility

Junior Faculty Development Award
2017, UNC

NIH Training Grant recipient: Biometry/Epidemiology Training Grant in Biostatistics
2013-2016, National Institutes of Health (NIH)

Representative Courses

BIOS 784 - Introduction to Computational Biology

BIOS 735 -Introduction to Statistical Computing

Research Activities

Gene regulation and dis-regulation in disease, genetic risk for neuropsychiatric disorders, cancer genomics and risk factors, data science, statistical software development, reproducibility in genomic data science. large scale hypothesis testing and effect size estimation

Key Publications

Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Anqi Zhu, Joseph G Ibrahim, and Michael I. Love (2018). Bioinformatics.
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Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Michael I. Love, John B Hogenesch, and Rafael A Irizarry (2016). Nature Biotechnology, 32(12), 1287-1291.
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Michael I. Love, Wolfgang Huber, and Simon Anders (2014). Genome Biology, 15(12).
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Education