metadata
dataset_info:
features:
- name: seq
dtype: string
- name: 'y'
dtype: float64
splits:
- name: train
num_bytes: 20823
num_examples: 248
- name: val
num_bytes: 4422
num_examples: 53
- name: test
num_bytes: 4545
num_examples: 54
download_size: 12650
dataset_size: 29790
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
license: mit
📊 Tc-Riboswitch Dataset
This dataset contains synthetic tetracycline (Tc) riboswitches along with their functional performance metrics. It's a curated resource for training machine learning models to predict gene expression from RNA sequences. The original dataset is from CodonBERT.
⁉️ Dataset Contents
- Sequence: The mRNA sequence of the synthetic riboswitch.
- Switching_factor: A numerical value that quantifies the riboswitch's regulatory effect. This value represents the change in gene expression in the presence versus the absence of tetracycline.
🎯 Purpose
This dataset serves as a benchmark for fine-tuning models on a regression task, predicting riboswitch behavior from its sequence. We used this dataset to fine-tune CDS-BART, a BART-based foundation model trained on massive mRNA sequences. Demonstrating its ability to perform downstream tasks related to mRNA regulation which are fine-tuned for various mRNA-related downstream task. CDS-BART available at GitHub
🔧Usage
from datasets import load_dataset
dataset = load_dataset('mogam-ai/CDS-BART-Tc-Riboswitches')
📚 Dataset Reference
- Groher, Ann-Christin, et al. "Tuning the performance of synthetic riboswitches using machine learning." ACS Synthetic Biology 8.1 (2018): 34-44.
- Li, Sizhen, et al. "CodonBERT large language model for mRNA vaccines." Genome research 34.7 (2024): 1027-1035.