--- 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.