biomap-research/localization_prediction

Dataset Card for Localization Prediction Dataset

Dataset Summary

The task of Protein Subcellular Localization Prediction bears substantial relevance in bioinformatics, owing to its contributions to proteomics research and its potential to augment our comprehension of protein function and disease mechanisms. In this task, the input to the model is an amino acid sequence of a protein, which is transformed into an output comprising a probability distribution over 10 unique subcellular localization categories.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and an integer label indicating which subcellular position the protein sequence locates at. See the localization prediction dataset viewer to explore more examples.

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':6}

The average for the seq and the label are provided below:

Feature	Mean Count
seq	544
label (0)	0.01
label (1)	0.10
label (2)	0.20
label (3)	0.03
label (4)	0.07
label (5)	0.06
label (6)	0.11
label (7)	0.34
label (8)	0.06
label (9)	0.02

Data Fields

• seq: a string containing the protein sequence
• label: an integer label indicating which subcellular position the protein sequence locates at.

Data Splits

The localization prediction dataset has 2 splits: train and test. Below are the statistics of the dataset.

Dataset Split	Number of Instances in Split
Train	6,622
Test	1,842

Source Data

Initial Data Collection and Normalization

The dataset applied for this task is derived from Uniprot, meticulously curated by DeepLoc.

Licensing Information

The dataset is released under the Apache-2.0 License.

Citation

If you find our work useful, please consider citing the following paper:

@misc{chen2024xtrimopglm,
  title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
  author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
  year={2024},
  eprint={2401.06199},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  note={arXiv preprint arXiv:2401.06199}
}