Name
Title | ||
|---|---|---|
GenoGAM 2.0: scalable and efficient implementation of genome-wide generalized additive models for gigabase-scale genomes. |
Abstract | ||
|---|---|---|
We have vastly improved the performance of the GenoGAM framework, opening up its application to all types of organisms. Moreover, our algorithmic improvements for fitting large GAMs could be of interest to the statistical community beyond the genomics field. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2238-7 | BMC Bioinformatics |
Keywords | Field | DocType |
ChIP-Seq,Generalized additive models,Genome-wide analysis,Sparse inverse subset algorithm,Transcription factors | Hierarchical Data Format,Biology,Parallel computing,Bioconductor,Statistical model,Solver,Genetics,Memory footprint,Generalized additive model,Scalability,Cholesky decomposition | Journal |
Volume | Issue | ISSN |
19 | 1 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georg Stricker | 1 | 0 | 0.34 |
| Mathilde Galinier | 2 | 0 | 0.34 |
| Julien Gagneur | 3 | 3 | 1.67 |