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@@ -196,25 +196,25 @@ The OPI dataset folder structure is as follows:
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  β”œβ”€β”€ AP
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  β”‚ β”œβ”€β”€ Function
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  β”‚ β”‚ β”œβ”€β”€ test
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- β”‚ β”‚ β”‚ β”œβ”€β”€ CASPSimilarSeq_function_valid.jsonl
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- β”‚ β”‚ β”‚ β”œβ”€β”€ IDFilterSeq_function_valid.jsonl
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- β”‚ β”‚ β”‚ └── UniProtSeq_function_valid.jsonl
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  β”‚ β”‚ └── train
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  β”‚ β”‚ β”œβ”€β”€ function_description_train.json
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  β”‚ β”‚ └── function_description_train_0.01.json
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  β”‚ β”œβ”€β”€ GO
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  β”‚ β”‚ β”œβ”€β”€ test
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- β”‚ β”‚ β”‚ β”œβ”€β”€ CASPSimilarSeq_go_valid.jsonl
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- β”‚ β”‚ β”‚ β”œβ”€β”€ IDFilterSeq_go_valid.jsonl
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- β”‚ β”‚ β”‚ └── UniProtSeq_go_valid.jsonl
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  β”‚ β”‚ └── train
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  β”‚ β”‚ β”œβ”€β”€ go_terms_train.json
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  β”‚ β”‚ └── go_terms_train_0.01.json
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  β”‚ └── Keywords
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  β”‚ β”œβ”€β”€ test
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- β”‚ β”‚ β”œβ”€β”€ CASPSimilarSeq_keywords_valid.jsonl
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- β”‚ β”‚ β”œβ”€β”€ IDFilterSeq_keywords_valid.jsonl
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- β”‚ β”‚ └── UniProtSeq_keywords_valid.jsonl
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  β”‚ └── train
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  β”‚ β”œβ”€β”€ keywords_train.json
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  β”‚ └── keywords_train_0.01.json
@@ -231,7 +231,7 @@ The OPI dataset folder structure is as follows:
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  β”‚ β”‚ └── gene_name_to_cancer_train.json
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  β”‚ └── gSymbol2Tissue
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  β”‚ β”œβ”€β”€ test
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- β”‚ β”‚ └── gene_symbol_to_tissue_valid.jsonl
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  β”‚ └── train
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  β”‚ └── gene_symbol_to_tissue_train.json
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  └── SU
@@ -243,20 +243,20 @@ The OPI dataset folder structure is as follows:
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  β”‚ β”œβ”€β”€ CLEAN_EC_number_train.json
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  β”œβ”€β”€ Fold_type-Remote
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  β”‚ β”œβ”€β”€ test
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- β”‚ β”‚ └── Remote_valid.jsonl
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  β”‚ └── train
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  β”‚ └── Remote_train.json
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  └── Subcellular_location
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  β”œβ”€β”€ test
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- β”‚ β”œβ”€β”€ location_valid.jsonl
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  └── train
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  └── location_train.json
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  ```
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  ## Dataset Creation
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- The OPI dataset is curated on our own by extracting key information from Swiss-Prot database. The detailed construction pipeline is depicted in the supplementary material of our manuscript which has been submitted to NeurIPS 2023 Datasets and Benchmarks. The following figure shows the general construction process.
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  ![image.png](./OPI_data.png)
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  ## License
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- The dataset is licensed under a Creative Commons Attribution Non Commercial 4.0 License.
 
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  β”œβ”€β”€ AP
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  β”‚ β”œβ”€β”€ Function
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  β”‚ β”‚ β”œβ”€β”€ test
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ CASPSimilarSeq_function_test.jsonl
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ IDFilterSeq_function_test.jsonl
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+ β”‚ β”‚ β”‚ └── UniProtSeq_function_test.jsonl
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  β”‚ β”‚ └── train
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  β”‚ β”‚ β”œβ”€β”€ function_description_train.json
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  β”‚ β”‚ └── function_description_train_0.01.json
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  β”‚ β”œβ”€β”€ GO
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  β”‚ β”‚ β”œβ”€β”€ test
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ CASPSimilarSeq_go_test.jsonl
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+ β”‚ β”‚ β”‚ β”œβ”€β”€ IDFilterSeq_go_test.jsonl
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+ β”‚ β”‚ β”‚ └── UniProtSeq_go_test.jsonl
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  β”‚ β”‚ └── train
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  β”‚ β”‚ β”œβ”€β”€ go_terms_train.json
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  β”‚ β”‚ └── go_terms_train_0.01.json
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  β”‚ └── Keywords
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  β”‚ β”œβ”€β”€ test
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+ β”‚ β”‚ β”œβ”€β”€ CASPSimilarSeq_keywords_test.jsonl
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+ β”‚ β”‚ β”œβ”€β”€ IDFilterSeq_keywords_test.jsonl
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+ β”‚ β”‚ └── UniProtSeq_keywords_test.jsonl
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  β”‚ └── train
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  β”‚ β”œβ”€β”€ keywords_train.json
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  β”‚ └── keywords_train_0.01.json
 
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  β”‚ β”‚ └── gene_name_to_cancer_train.json
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  β”‚ └── gSymbol2Tissue
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  β”‚ β”œβ”€β”€ test
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+ β”‚ β”‚ └── gene_symbol_to_tissue_test.jsonl
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  β”‚ └── train
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  β”‚ └── gene_symbol_to_tissue_train.json
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  └── SU
 
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  β”‚ β”œβ”€β”€ CLEAN_EC_number_train.json
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  β”œβ”€β”€ Fold_type-Remote
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  β”‚ β”œβ”€β”€ test
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+ β”‚ β”‚ └── Remote_test.jsonl
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  β”‚ └── train
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  β”‚ └── Remote_train.json
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  └── Subcellular_location
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  β”œβ”€β”€ test
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+ β”‚ β”œβ”€β”€ location_test.jsonl
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  └── train
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  └── location_train.json
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  ```
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  ## Dataset Creation
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+ The OPI dataset is curated on our own by extracting key information from [Swiss-Prot](https://www.uniprot.org/uniprotkb?facets=reviewed%3Atrue&query=%2A) database. The detailed construction pipeline is depicted in the supplementary material of our manuscript which has been submitted to NeurIPS 2023 Datasets and Benchmarks. The following figure shows the general construction process.
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  ![image.png](./OPI_data.png)
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  ## License
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+ The dataset is licensed under a Creative Commons Attribution Non Commercial 4.0 License. The use of this dataset should also abide by the original [License & Disclaimer](https://www.uniprot.org/help/license) and [Privacy Notice](https://www.uniprot.org/help/privacy) of UniProt.