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Phil H. Lee

dblp:116/4559 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0003-1770-3100ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
gene set enrichment analysis
0.112012
INRICH: interval-based enrichment analysis for genome-wide association studies · Bioinform. 2012
Bioinformatics and computational biology › genomics
genome-wide association study
0.112012
INRICH: interval-based enrichment analysis for genome-wide association studies · Bioinform. 2012
Bioinformatics and computational biology › genomics › genome-wide association study
pathway-based association analysis
0.112012
INRICH: interval-based enrichment analysis for genome-wide association studies · Bioinform. 2012

Methods — techniques the papers use, named apart from their topics

interval-based enrichment · 0.1genomic permutation · 0.1
YearPublicationVenuePosition
2023 COMMUTE: Communication-efficient transfer learning for multi-site risk prediction
Tian Gu, Phil H. Lee, Rui Duan 0004
J. Biomed. Informatics2
2012 INRICH: interval-based enrichment analysis for genome-wide association studies
abstract
SUMMARY: Here we present INRICH (INterval enRICHment analysis), a pathway-based genome-wide association analysis tool that tests for enriched association signals of predefined gene-sets across independent genomic intervals. INRICH has wide applicability, fast running time and, most importantly, robustness to potential genomic biases and confounding factors. Such factors, including varying gene size and single-nucleotide polymorphism density, linkage disequilibrium within and between genes and overlapping genes with similar annotations, are often not accounted for by existing gene-set enrichment methods. By using a genomic permutation procedure, we generate experiment-wide empirical significance values, corrected for the total number of sets tested, implicitly taking overlap of sets into account. By simulation we confirm a properly controlled type I error rate and reasonable power of INRICH under diverse parameter settings. As a proof of principle, we describe the application of INRICH on the NHGRI GWAS catalog. AVAILABILITY: A standalone C++ program, user manual and datasets can be freely downloaded from: http://atgu.mgh.harvard.edu/inrich/.
Phil H. Lee, Colm O'Dushlaine, Brett Thomas, Shaun Purcell
Bioinform.1