update readme with more details

README.md

@@ -1,8 +1,16 @@
-# Dataset Card for ClinVar Exomic Downstream Dataset
+---
+tags:
+- biology
+- medical
+pretty_name: ExomeBench
+size_categories:
+- 1K<n<10K
+---
+# Dataset Card for the ExomeBench Dataset
 
-The ClinVar dataset is derived from ClinVar, a publicly accessible database maintained by the [National Center for Biotechnology Information (NCBI)](https://www.ncbi.nlm.nih.gov/clinvar). 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).
+The ExomeBench dataset is derived from ClinVar, a publicly accessible database maintained by the [National Center for Biotechnology Information (NCBI)](https://www.ncbi.nlm.nih.gov/clinvar). 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)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/).
 
-This dataset provides a valuable resource for researchers and practitioners working on genetic variant analysis and its clinical implications. 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.
+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.
 
 ## Dataset Details
 
@@ -15,50 +23,45 @@ This dataset provides a valuable resource for researchers and practitioners work
 
 #### Data Filtering
 
-1. **Exome-Specific Regions**: Filter the variants to include only those located in exome-specific regions.
+1. **Assertion Criteria**: We include only variants with at least one submitter providing an interpretation and satisfying the assertion criteria for reliability.  
+2. **Variant Type**: Only single-nucleotide variants (SNVs) are selected.  
+2. **Exome-Specific Regions**: Filter the variants to include only those located in exome-specific regions.
 
 #### Sequence Generation
 
-1. **Human Reference Genome (HRG)**: For each variant, generate input sequences from the HRG.
+1. **Human Reference Genome (HRG)**: For each variant, generate input sequences from the HRG using genome **ADD HERE**.
 2. **Sequence Length**: The length of the sequences is a parameter, typically set to 100 base pairs (bp).
-3. **Variant Positioning**: The variant is centered within the sequence.
+3. **Variant Positioning**: The variant is centered within the sequence, which is read in from a FASTA file.
 
 ### Tasks
 
 There are 7 tasks created using the ClinVar data.
 
-1. **Exome Pathogenicity Prediction**: Same as above, but variants are stratified between train/test sets to ensure that variants from the same gene don't appear in both. 
-2. **Exome Variants and Non-Variants**: Predict whether an exome sequence is a mutation or not. 
+1. **Exome Pathogenicity Prediction**: Predict the pathogenicity of an exome variant sequence (pathogenic, likely pathogenic, likely benign, benign). Variants are stratified between train/test sets to ensure that variants from the same gene don't appear in both.
+2. **Exome Variants and Non-Variants**: Predict whether an exome sequence represents a known SNV or a non-variant reference sequence.
 3. **Phenotype Prediction**:
-    1. **Cancer-Predisposing Syndrome**: Predict whether a variant is associated with the phenotype of Hereditary Cancer-Predisposing Syndrome. 
+    1. **Cancer-Predisposing Syndrome**: Predict whether a variant is associated with the phenotype of Hereditary Cancer-Predisposing Syndrome.
     2. **Cardiovascular Phenotypes**: Predict whether a variant is associated with cardiovascular phenotypes.
-5. **Gene Prediction**:
+4. **Gene Prediction**:
     1. **BRCA**: Predict whether a variant belongs to BRCA1, BRCA2, or neither (breast cancer genes).
-    2. **TTN**: Predict whether a variant belongs to the TTN gene. 
-    3. **Top Ten Genes**: Predict whether a variant belongs to one of five (ten?) possible gene pools. 
-
+    2. **TTN**: Predict whether a variant belongs to the TTN gene.
+    3. **Top Five Genes**: Predict whether a variant belongs to one of five most common possible gene pools.
 
 - **Curated by:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Language(s) (NLP):** Python
 - **License:** [More Information Needed]
 
-
 ## Uses
 
-<!-- Address questions around how the dataset is intended to be used. -->
+This dataset is intended for models that aim to test their ability on exome-specific data. With more genomic models now focusing on exome regions for training, there is a need for benchmarks that verify whether these models truly learn exome-related features—particularly since many existing benchmarks overlook exome data, even though most disease-associated variants lie in protein-coding regions.
 
 ### Direct Use
 
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
+These tasks are split into train/test sets and are designed to fine-tune larger models, which can then be evaluated using metrics such as AUC. The dataset encompasses a range of tasks—from assessing general exome-specific changes to identifying gene-specific variants and predicting pathogenicity—providing a broad overview of model performance on clinically relevant exome data.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
+The dataset is for research and benchmarking only; it should not be used as a standalone diagnostic tool. More so, ClinVar may still contain some noise or inconsistencies in variant annotations.
 
 ## Dataset Card Contact