CoolFace
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biomap-research/material_production

Dataset Card for Material Production Dataset Dataset Summary The task is to predict whether a protein sequence fails at the protein material stage. Dataset Structure Data Instances For each instance, there is a string representing the protein sequence and an integer label indicating whether a protein sequence fails at the protein material stage. See the material production dataset viewer to explore more examples.… See the full description on the dataset page: https://huggingface.co/datasets/biomap-research/material_production.

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Dataset Card

Dataset Card for Material Production Dataset

Dataset Summary

The task is to predict whether a protein sequence fails at the protein material stage.

Dataset Structure

Data Instances

For each instance, there is a string representing the protein sequence and an integer label indicating whether a protein sequence fails at the protein material stage. See the material production dataset viewer to explore more examples.

{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':1}

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

FeatureMean Count
seq315
label (0)0.3
label (1)0.7

Data Fields

  • —seq: a string containing the protein sequence
  • —label: an integer label indicating whether a protein sequence fails at the protein material stage.

Data Splits

The material production dataset has 2 splits: train and test. Below are the statistics of the dataset.

Dataset SplitNumber of Instances in Split
Train23,339
Test4,791

Source Data

Initial Data Collection and Normalization

The dataset is collected from PredPPCrys.

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}
}