Update README.md

README.md

@@ -240,13 +240,9 @@ from datasets import load_dataset
 
 DATASET_SUBSET = "ancestry_prediction"
 
-# Dataset subset should be from one of the available tasks: 'ancestry_prediction'
-#                                                           'non_coding_pathogenicity'
-#                                                           'expression'
-#                                                           'common_vs_rare'
-#                                                           'coding_pathogenicity'
-#                                                           'meqtl'
-#                                                           'sqtl'
+# Dataset subset should be from one of the available tasks:
+# ['ancestry_prediction', 'non_coding_pathogenicity', 'expression',
+#                         'common_vs_rare', 'coding_pathogenicity', 'meqtl', 'sqtl']
 
 ds = load_dataset(
       "m42-health/variant-benchmark",
@@ -270,7 +266,7 @@ print(ds)
 
 - **Coding pathogenicity assessment:** `subset: coding_pathogenicity`<br/>
 Accurate prediction of pathogenic coding variants is fundamental to precision medicine and clinical genomics. 
-For this task, we use the AlphaMissense dataset~\citep{cheng2023alphamissense}, which provides a comprehensive catalog of coding variants annotated for pathogenicity.
+For this task, we use the [AlphaMissense](https://github.com/google-deepmind/alphamissense) dataset, which provides a comprehensive catalog of coding variants annotated for pathogenicity.
 
 - **Noncoding pathogenicity assessment:** `subset: non_coding_pathogenicity`<br/>
 Pathogenic variants in noncoding regions significantly impact gene regulation, influencing many complex traits and diseases. 
@@ -278,15 +274,15 @@ We assess this using the [BEND dataset](https://github.com/frederikkemarin/BEND)
 
 - **Expression effect prediction:** `subset: expression`<br/>
 Variant-driven changes in gene expression contribute to phenotypic diversity and disease processes. 
-To quantify these effects, we use gene expression data from DeepSea, which provides variant-associated regulatory annotations.
+To quantify these effects, we use gene expression data from [DeepSea](https://www.nature.com/articles/nmeth.3547), which provides variant-associated regulatory annotations.
 
 - **Alternative splicing:** `subset: sqtl`<br/>
 Variant-induced alternative splicing contributes significantly to human proteomic diversity and affects biological processes and diseases. 
-We evaluate splicing-related variant effects using an sQTL dataset derived from sqtlSeeker2, containing over one million variant-tissue pairs.
+We evaluate splicing-related variant effects using an [sQTL dataset](https://www.nature.com/articles/s41467-020-20578-2) derived from sqtlSeeker2, containing over one million variant-tissue pairs.
 
 - **DNA methylation:** `subset: meqtl`<br/>
 Genomic variants can influence DNA methylation patterns, affecting gene regulation and disease susceptibility.
-For this task, we utilize meQTL data from the GRASP database, which links genetic variants to methylation changes.
+For this task, we utilize meQTL data from the [GRASP database](https://pubmed.ncbi.nlm.nih.gov/24931982/), which links genetic variants to methylation changes.
 
 - **Ancestry classification:** `subset: ancestry_prediction`<br/>
 Genomic variation encodes population structure, informing studies in evolutionary biology and disease susceptibility.