Dnabert
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Deepvariant | 2,978 | 4 months ago | 8 | bsd-3-clause | Python | |||||
| DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. | ||||||||||
| Dnabert | 471 | 6 months ago | 60 | apache-2.0 | Python | |||||
| DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome | ||||||||||
| Mirdeep2 | 120 | 5 months ago | 2 | gpl-3.0 | Perl | |||||
| Discovering known and novel miRNAs from small RNA sequencing data | ||||||||||
| Tensorflow Neuroevolution | 119 | 6 months ago | 5 | September 03, 2020 | apache-2.0 | Python | ||||
| Neuroevolution Framework for Tensorflow 2.x focusing on modularity and high-performance. Preimplements NEAT, DeepNEAT, CoDeepNEAT | ||||||||||
| Deepsimulator | 76 | 3 years ago | 8 | C | ||||||
| The first deep learning based Nanopore simulator which can simulate the process of Nanopore sequencing. | ||||||||||
| Clair | 74 | 3 years ago | 4 | bsd-3-clause | Python | |||||
| Clair: Exploring the limit of using deep neural network on pileup data for germline variant calling | ||||||||||
| Visual7w Qa Models | 63 | 5 years ago | 4 | mit | Lua | |||||
| Visual7W visual question answering models | ||||||||||
| Simsg | 51 | 2 years ago | 2 | apache-2.0 | Python | |||||
| Semantic Image Manipulation using Scene Graphs (CVPR 2020) | ||||||||||
| Deepsomatic | 45 | 6 months ago | bsd-3-clause | |||||||
| DeepSomatic is an analysis pipeline that uses a deep neural network to call somatic variants from tumor-normal sequencing data. | ||||||||||
| Genomelake | 37 |  | 1 | 1 | 4 years ago | 4 | April 14, 2018 | 4 | bsd-3-clause | Python |
| Simple and efficient access to genomic data for deep learning models. | ||||||||||
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