Michael I. Love

Molecular Biology · 2014–2021

A researcher in the Science corpus: 11 papers; trusts 42 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 248 citations into it; #1,500 of 17,773 by trust here, #1,158 by citations
• Cancer Research — 48 citations into it; #119 of 1,780 by trust here, #129 by citations
• Genetics — 43 citations into it
• Statistics and Probability — 35 citations into it
• Artificial Intelligence — 19 citations into it
• Management Science and Operations Research — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Michael I. Love trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Gordon K. Smyth
2. Simon Anders
3. Mark D. Robinson
4. Wolfgang Huber
5. Yunshun Chen
6. Davis J. McCarthy
7. Davide Risso
8. Charity W. Law
9. Charlotte Soneson
10. Michael F. Lawrence

Who trusts Michael I. Love overall

1. K. Van den Berge
2. Romain Lopez
3. Gabriel E. Hoffman
4. Yunshun Chen
5. Aaron T. L. Lun
6. Tallulah S. Andrews
7. Saket Kumar Choudhary
8. Wei Vivian Li
9. Á. Bartha
10. Charlotte Soneson

Papers from Michael I. Love's perspective

Ordered by how strongly the researchers Michael I. Love trusts endorse each paper. Same corpus, a different order for every researcher.

1. A scaling normalization method for differential expression analysis of RNA-seq data (Genome Biology)
2. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Genome Biology)
3. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. (Genome Research)
4. Detecting differential usage of exons from RNA-seq data (Genome Research)
5. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (Statistical Applications in Genetics and Molecular Biology)
6. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. (Biostatistics)
7. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)
8. A comparison of methods for differential expression analysis of RNA-seq data (BMC Bioinformatics)
9. Moderated statistical tests for assessing differences in tag abundance (Bioinform.)
10. Understanding mechanisms underlying human gene expression variation with RNA sequencing (Nature)

Their papers

• Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference (2017) — 8,742 citations
• Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014) — 39,074 citations
• Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences (2015) — 3,582 citations
• RNA-Seq workflow: gene-level exploratory analysis and differential expression (2015) — 405 citations
• MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens (2014) — 2,340 citations
• A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines (2021) — 113 citations
• Tximeta: Reference sequence checksums for provenance identification in RNA-seq (2019) — 305 citations
• Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018) — 1,744 citations
• RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018) — 116 citations
• Orchestrating high-throughput genomic analysis with Bioconductor (2015) — 3,336 citations
• Love MI, Huber W, Anders S.. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biol 15: 550 (2014) — 524 citations