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---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 5339565
num_examples: 21446
- name: valid
num_bytes: 1335010
num_examples: 5362
- name: test
num_bytes: 6775269
num_examples: 27217
download_size: 2163187
dataset_size: 13449844
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- text-classification
tags:
- chemistry
- biology
- medical
size_categories:
- 10K<n<100K
---
# Dataset Card for Fluorescence Prediction Dataset
### Dataset Summary
The Fluorescence Prediction task focuses on predicting the fluorescence intensity of green fluorescent protein mutants, a crucial function in biology that allows researchers to infer the presence of proteins within cell lines and living organisms. This regression task utilizes training and evaluation datasets that feature mutants with three or fewer mutations, contrasting the testing dataset, which comprises mutants with four or more mutations.
## Dataset Structure
### Data Instances
For each instance, there is a string representing the protein sequence and a float value indicating the fluorescence score of the protein sequence. See the [fluorescence prediction dataset viewer](https://huggingface.co./datasets/Bo1015/fluorescence_prediction/viewer) to explore more examples.
```
{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':3.6}
```
The average for the `seq` and the `label` are provided below:
| Feature | Mean Count |
| ---------- | ---------------- |
| seq | 237 |
| label | 2.63 |
### Data Fields
- `seq`: a string containing the protein sequence
- `label`: a float value indicating the fluorescence score of the protein sequence.
### Data Splits
The fluorescence prediction dataset has 3 splits: _train_, _valid_ and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 21,446 |
| Valid | 5,362 |
| Test | 27,217 |
### Source Data
#### Initial Data Collection and Normalization
The datasets is collected from the [TAPE](https://github.com/songlab-cal/tape).
### Licensing Information
The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### 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}
}
``` |