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@@ -14,13 +14,19 @@ task_categories:
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  <br />
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  <div align="center">
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- <h1 align="center">ExomeBench: A Benchmark for Clinical Variant Interpretation in Exome Regions 🧬</h1>
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  <p align="center">
 
 
 
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  <a href="https://www.researchsquare.com/article/rs-6115078/v1">
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  Paper
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  </a>
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  |
 
 
 
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  <a href="https://github.com/Cerebras/exome_bench">
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  GitHub
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  </a>
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  ## 1. Project Overview
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- The **ExomeBench** dataset is derived from [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar) [(Nov 2024 release)](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/archive/variant_summary_2024-11.txt.gz), a publicly accessible database maintained by the National Center for Biotechnology Information (NCBI). ClinVar provides comprehensive information on the clinical significance of genetic variants and their associations with human diseases. This dataset focuses on variants located in exome-specific regions and includes input sequences generated from the [Human Reference Genome (HRG, GRCh38)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/).
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  This dataset provides a valuable resource for researchers and practitioners working on genetic variant analysis and its clinical implications. Exome-specific regions are critically important because they encompass all protein-coding regions of the genome, where disease-associated variants are most likely to occur. By focusing on exome-specific regions and using sequences from the Human Reference Genome, this dataset enables robust evaluation of models on clinically significant tasks.
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  ## 2. Dataset Details
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  <td align="center"><strong>0.877</strong></td>
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  <td align="center"><strong>0.996</strong></td>
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  </tr>
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- <tr>
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- <td>DNABERT-2</td>
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  <td align="center">0.162</td>
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  <td align="center">0.876</td>
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  <td align="center">0.549</td>
@@ -163,7 +169,7 @@ ExomeBench includes **five supervised tasks**, each framed as a classification p
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  <td align="center">0.996</td>
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  </tr>
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  <tr>
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- <td>HyenaDNA</td>
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  <td align="center">0.135</td>
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  <td align="center">0.816</td>
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  <td align="center">0.445</td>
@@ -171,13 +177,12 @@ ExomeBench includes **five supervised tasks**, each framed as a classification p
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  <td align="center">0.994</td>
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  </tr>
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  <tr>
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- <td>NT-Multispecies-2.5B</td>
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  <td align="center">0.306</td>
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  <td align="center">0.624</td>
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  <td align="center">0.293</td>
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  <td align="center">0.422</td>
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  <td align="center">0.991</td>
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- </tr>
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  </tbody>
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  </table>
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  <br />
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  <div align="center">
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+ <h1 align="center">ExomeBench Dataset: A Benchmark Dataset for Clinical Variant Interpretation in Exome Regions 🧬</h1>
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  <p align="center">
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+ <a href="https://www.researchsquare.com/article/rs-6115078/v1">
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+ <img src="https://info.arxiv.org/brand/images/brand-supergraphic.jpg" alt="arxiv" width="20" height="20" style="vertical-align: middle; margin-right: 0px;">
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+ </a>
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  <a href="https://www.researchsquare.com/article/rs-6115078/v1">
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  Paper
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  </a>
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  |
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+ <a href="https://www.github.com/Cerebras/exome_bench">
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+ <img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub" width="20" height="20" style="vertical-align: middle; margin-right: 0px;">
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+ </a>
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  <a href="https://github.com/Cerebras/exome_bench">
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  GitHub
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  </a>
 
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  ## 1. Project Overview
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+ The **ExomeBench** dataset is derived from [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar) [(March 2024 release)](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/), a publicly accessible database maintained by the National Center for Biotechnology Information (NCBI). ClinVar provides comprehensive information on the clinical significance of genetic variants and their associations with human diseases. This dataset focuses on variants located in exome-specific regions and includes input sequences generated from the [Human Reference Genome (HRG, GRCh38)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/).
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  This dataset provides a valuable resource for researchers and practitioners working on genetic variant analysis and its clinical implications. Exome-specific regions are critically important because they encompass all protein-coding regions of the genome, where disease-associated variants are most likely to occur. By focusing on exome-specific regions and using sequences from the Human Reference Genome, this dataset enables robust evaluation of models on clinically significant tasks.
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+ Code to fine-tune and evaluate models on this dataset using the Hugging Face Transformers library is available in [ExomeBench GitHub Repository](https://www.github.com/Cerebras/exome_bench).
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  ## 2. Dataset Details
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  <td align="center"><strong>0.877</strong></td>
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  <td align="center"><strong>0.996</strong></td>
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  </tr>
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+ <td><a href="https://huggingface.co/zhihan1996/DNABERT-2-117M">DNABERT-2-117M</a></td>
 
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  <td align="center">0.162</td>
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  <td align="center">0.876</td>
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  <td align="center">0.549</td>
 
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  <td align="center">0.996</td>
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  </tr>
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  <tr>
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+ <td><a href="https://huggingface.co/LongSafari/hyenadna-tiny-1k-seqlen-hf">HyenaDNA-Tiny-1k</a></td>
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  <td align="center">0.135</td>
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  <td align="center">0.816</td>
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  <td align="center">0.445</td>
 
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  <td align="center">0.994</td>
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  </tr>
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  <tr>
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+ <td><a href="https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-multi-species">NT-Multispecies-2.5B</a></td>
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  <td align="center">0.306</td>
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  <td align="center">0.624</td>
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  <td align="center">0.293</td>
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  <td align="center">0.422</td>
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  <td align="center">0.991</td>
 
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  </tbody>
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  </table>
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