| --- |
| license: mit |
| task_categories: |
| - text-classification |
| tags: |
| - protein |
| - enzyme |
| - bioinformatics |
| - protein-function-prediction |
| pretty_name: UNKAI Protein Pair Dataset |
| --- |
| |
| # UNKAI Protein Pair Dataset |
|
|
| This repository contains protein-pair datasets used for UNKAI, a binary classification model that predicts whether two proteins are associated with the same enzymatic reaction. |
|
|
| Three dataset variants are provided: |
|
|
| * `original` |
| * `seen_unseen` |
| * `strict` |
|
|
| Each dataset is divided into training, validation, and test splits. |
|
|
| ## Data format |
|
|
| Each TSV file contains two columns: |
|
|
| ```text |
| pair label |
| A0RQT4_C1EZA3 1 |
| ``` |
|
|
| ### `pair` |
|
|
| A pair of protein accession IDs represented as: |
|
|
| ```text |
| PROTEIN1_PROTEIN2 |
| ``` |
|
|
| ### `label` |
|
|
| Binary classification label: |
|
|
| ```text |
| 1 = same enzymatic reaction |
| 0 = different enzymatic reaction |
| ``` |
|
|
| ## Repository structure |
|
|
| ```text |
| original/ |
| ├── train.tsv |
| ├── validation.tsv |
| └── test.tsv |
| |
| seen_unseen/ |
| ├── train.tsv |
| ├── validation.tsv |
| └── test.tsv |
| |
| strict/ |
| ├── train.tsv |
| ├── validation.tsv |
| └── test.tsv |
| ``` |
|
|
| # Dataset variants |
|
|
| ## Original |
|
|
| The original dataset uses a pair-level random split. |
|
|
| The positive and negative classes are balanced within each split. |
|
|
| | Split | Total | Positive | Negative | |
| | ---------- | ------: | -------: | -------: | |
| | Train | 140,000 | 70,000 | 70,000 | |
| | Validation | 30,000 | 15,000 | 15,000 | |
| | Test | 30,000 | 15,000 | 15,000 | |
|
|
| No exact protein-pair duplicates are shared between train, validation, and test. |
|
|
| This split provides the least restrictive evaluation setting among the three released variants. |
|
|
| ## Seen-unseen |
|
|
| The seen-unseen dataset was constructed using protein clusters. |
|
|
| Protein clusters are first assigned to train, validation, or test groups. |
|
|
| Pairs are then assigned according to the following rules: |
|
|
| ### Training |
|
|
| Both proteins must belong to clusters assigned to training. |
|
|
| ```text |
| train cluster + train cluster |
| ``` |
|
|
| ### Validation |
|
|
| Exactly one protein belongs to a training cluster and the other belongs to a validation cluster. |
|
|
| ```text |
| train cluster + validation cluster |
| ``` |
|
|
| ### Test |
|
|
| Exactly one protein belongs to a training cluster and the other belongs to a test cluster. |
|
|
| ```text |
| train cluster + test cluster |
| ``` |
|
|
| Therefore, each validation/test pair contains one side from a cluster distribution observed during training and one side from an unseen cluster distribution. |
|
|
| The released dataset contains: |
|
|
| | Split | Total | Positive | Negative | |
| | ---------- | -----: | -------: | -------: | |
| | Train | 73,463 | 37,765 | 35,698 | |
| | Validation | 10,000 | 5,000 | 5,000 | |
| | Test | 10,000 | 5,000 | 5,000 | |
|
|
| No exact protein-pair duplicates are shared between train, validation, and test. |
|
|
| Same-cluster pairs are excluded from the released seen-unseen setting because validation and test pairs necessarily consist of proteins from two differently assigned clusters. |
|
|
| Exact protein accessions from training may appear on the seen side of validation/test pairs by design. |
|
|
| ## Strict |
|
|
| The strict dataset provides the strongest cluster-separation setting. |
|
|
| Protein clusters are first divided into mutually exclusive train, validation, and test groups. |
|
|
| A protein pair is included in a split only when both proteins belong to clusters assigned to that same split. |
|
|
| Conceptually: |
|
|
| ```text |
| Train: |
| train cluster + train cluster |
| |
| Validation: |
| validation cluster + validation cluster |
| |
| Test: |
| test cluster + test cluster |
| ``` |
|
|
| Therefore, clusters used in validation and test are not present in the training cluster set. |
|
|
| The released strict dataset contains: |
|
|
| | Split | Total | Positive | Negative | |
| | ---------- | -----: | -------: | -------: | |
| | Train | 67,699 | 32,236 | 35,463 | |
| | Validation | 10,000 | 5,000 | 5,000 | |
| | Test | 10,000 | 5,000 | 5,000 | |
|
|
| No exact protein-pair duplicates are shared between train, validation, and test. |
|
|
| # Sampling controls |
|
|
| The cluster-based datasets were constructed with additional sampling controls to reduce excessive representation of individual proteins or clusters. |
|
|
| Reverse protein pairs are treated as identical: |
|
|
| ```text |
| A_B == B_A |
| ``` |
|
|
| This is appropriate for UNKAI because its pair representation is based on: |
|
|
| ```text |
| |v1 - v2| |
| ``` |
|
|
| which is symmetric with respect to protein order. |
|
|
| During final dataset selection, the following frequency limits were used: |
|
|
| | Constraint | Maximum | |
| | -------------------------------- | ------: | |
| | Pairs per protein accession | 10 | |
| | Pairs per cluster | 80 | |
| | Pairs per cluster-pair per label | 15 | |
|
|
| During candidate collection, at most 40 examples per cluster-pair per label were retained before final sampling. |
|
|
| ## Cluster split |
|
|
| Protein clusters were randomly divided using seed `42`. |
|
|
| Approximately: |
|
|
| ```text |
| 80% training clusters |
| 10% validation clusters |
| 10% test clusters |
| ``` |
|
|
| were assigned before constructing the strict and seen-unseen datasets. |
|
|
| # Pair overlap verification |
|
|
| The released TSV files were independently checked for exact pair overlap between splits. |
|
|
| For all three dataset variants: |
|
|
| ```text |
| train ∩ validation = 0 |
| train ∩ test = 0 |
| validation ∩ test = 0 |
| ``` |
|
|
| # Relationship between the datasets |
|
|
| The three datasets are intended to represent increasingly challenging generalization settings. |
|
|
| ```text |
| Original |
| | |
| | pair-level split |
| v |
| Seen-unseen |
| | |
| | one unseen cluster side |
| v |
| Strict |
| | |
| | completely cluster-separated |
| v |
| stronger distribution shift |
| ``` |
|
|
| The strict dataset is therefore intended to provide a more conservative estimate of generalization to proteins from clusters not represented during training. |
|
|
| # Models |
|
|
| Pretrained UNKAI checkpoints are available separately: |
|
|
| ```text |
| ukaikotaro/UNKAI |
| ``` |
|
|
| Source code and inference utilities: |
|
|
| https://github.com/ukai3313/UNKAI |
|
|
| # Limitations |
|
|
| The labels describe enzymatic-reaction association according to the source data used to construct the protein pairs. |
|
|
| The datasets should not be interpreted as a complete representation of all protein functions or all enzyme reaction relationships. |
|
|
| Results obtained using different splitting strategies are not directly interchangeable. In particular, random pair-level evaluation can be substantially easier than evaluation under cluster separation. |
|
|
| # Citation |
|
|
| Citation information for the associated publication will be added here. |
|
|
| # License |
|
|
| MIT License. |
|
|
|
|
