Demo Data from VLDB 2026 / reviewers in the wild / expert
Lakshmi Kuttippurathu
dblp:85/9181
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2011
0000-0001-6612-9040ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › epigenomics
ChIP-seq analysis |
0.1 | 1 | 2011 | CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments · Bioinform. 2011 |
Bioinformatics and computational biology
genomics |
0.1 | 1 | 2011 | CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments · Bioinform. 2011 |
Bioinformatics and computational biology › sequence analysis › motif discovery
transcription factor binding motif discovery |
0.1 | 1 | 2011 | CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments · Bioinform. 2011 |
Methods — techniques the papers use, named apart from their topics
motif scanning · 0.1enrichment testing · 0.1de novo motif discovery · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experimentsabstractUNLABELLED: CompleteMOTIFs (cMOTIFs) is an integrated web tool developed to facilitate systematic discovery of overrepresented transcription factor binding motifs from high-throughput chromatin immunoprecipitation experiments. Comprehensive annotations and Boolean logic operations on multiple peak locations enable users to focus on genomic regions of interest for de novo motif discovery using tools such as MEME, Weeder and ChIPMunk. The pipeline incorporates a scanning tool for known motifs from TRANSFAC and JASPAR databases, and performs an enrichment test using local or precalculated background models that significantly improve the motif scanning result. Furthermore, using the cMOTIFs pipeline, we demonstrated that multiple transcription factors could cooperatively bind to the upstream of important stem cell differentiation regulators. AVAILABILITY: http://cmotifs.tchlab.org. Lakshmi Kuttippurathu, Michael Hsing, Yongchao Liu 0004, Bertil Schmidt, Douglas L. Maskell, Kyungjoon Lee, Aibin He, William T. Pu, Sek Won Kong |
Bioinform. | 1 |