Downloads provided by UsageCounts
The code to the preprint https://www.biorxiv.org/content/10.1101/2023.07.19.549677v1. Python was used for the model, performance assessment and data generation. R was used for scripting and data visualisation. All input data for the R scripts are separately provided, so that the data-intense and more intense computational steps do not have to be repeated. For the Python code, the folder finetuning_tasks has to be combined after decompression. It had to be split into four folders due to uploading problems. A tutorial on how to use GROVER as a foundation model can be found at: https://doi.org/10.5281/zenodo.8373159 The pretrained model can be found at: https://doi.org/10.5281/zenodo.8373117 The data for the tokenised genome are at: https://doi.org/10.5281/zenodo.8373053
GROVER, DNA Language Models
GROVER, DNA Language Models
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 37 | |
| downloads | 9 |

Views provided by UsageCounts
Downloads provided by UsageCounts