license: cc-by-nc-4.0
task_categories:
- tabular-classification
tags: - biology
- medical
- genomics
- genetics
- bioinformatics
pretty_name: Genetic Variant Pathogenicity Classification
size_categories: - 10K<n<100K
Genetic Variant Pathogenicity Dataset
Dataset Description
This dataset contains annotated genetic variants (mutations) designed for tabular binary classification tasks. The objective is to predict whether a given genetic variant is Pathogenic (disease-causing) or Benign (harmless) based on a rich set of bioinformatics annotations, evolutionary conservation scores, and functional prediction tools.
- Task: Binary Classification
- Target Column:
Pathologic/Benign
Dataset Structure
The dataset is pre-split into train and test sets, making it ready for immediate machine learning modeling. The class distribution is highly balanced.
| Split | Number of Rows | Benign Count | Pathogenic Count |
|---|---|---|---|
| Train | 7,856 | 3,940 | 3,916 |
| Test | 4,910 | 2,460 | 2,450 |
Key Features
The dataset consists of 69 columns. While it includes extensive biological data, some of the most critical feature categories include:
- Variant Identifiers:
Chrom,Position,Ref Base,Alt Base,Gene - Molecular Consequences:
Sequence Ontology(e.g., missense_variant),cDNA change,Protein Change - Population Frequencies: Allele frequencies from the 1000 Genomes Project and ESP6500.
- Functional Prediction Scores:
CADD Exome Score,PolyPhen-2,SIFT,REVEL Score - Conservation Scores:
GERP++,PhyloP,SiPhy - Target Label:
Pathologic/Benign(Values: "Benign" or "Pathogenic")
Usage
You can easily load and explore this dataset using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("bedylmz/missense-variant-effects")
# View the dataset structure
print(dataset)
# Convert the train split to a Pandas DataFrame for easy manipulation
df_train = dataset['train'].to_pandas()
print(df_train['Pathologic/Benign'].value_counts())