Name
Title | ||
|---|---|---|
YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU. |
Abstract | ||
|---|---|---|
Motivation: Motif discovery in large biopolymer sequence datasets can be computationally demanding, presenting significant challenges for discovery in omics research. MEME, arguably one of the most popular motif discovery software, takes quadratic time with respect to dataset size, leading to excessively long runtimes for large datasets. Therefore, there is a demand for fast programs that can generate results of the same quality as MEME. Results: Here we describe YAMDA, a highly scalable motif discovery software package. It is built on Pytorch, a tensor computation deep learning library with strong GPU acceleration that is highly optimized for tensor operations that are also useful for motifs. YAMDA takes linear time to find motifs as accurately as MEME, completing in seconds or minutes, which translates to speedups over a thousandfold. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/bty396 | BIOINFORMATICS |
Field | DocType | Volume |
Tensor,Biology,Parallel computing,Software,Acceleration,Artificial intelligence,Deep learning,Genetics,Time complexity,Speedup,Computation,Scalability | Journal | 34 |
Issue | ISSN | Citations |
20 | 1367-4803 | 1 |
PageRank | References | Authors |
0.36 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Quang | 1 | 47 | 3.23 |
| Yuanfang Guan | 2 | 2 | 2.44 |
| Stephen C. J. Parker | 3 | 1 | 0.70 |