README.md

metadata

license: mit
task_categories:
  - token-classification
language:
  - en
tags:
  - protein
  - bioinformatics
  - secondary-structure
  - deep-learning
size_categories:
  - 1K<n<10K

Protein Secondary Structure Prediction Dataset (NPPE2)

Dataset Description

This dataset contains protein sequences with their corresponding secondary structure labels for both Q8 (8-class) and Q3 (3-class) classification tasks.

Dataset Statistics

Split	Sequences
Train	7262
Test	1816

Features

• id: Unique identifier for each protein sequence
• seq: Amino acid sequence (20 standard amino acids)
• sst8: 8-class secondary structure labels (Q8)
• sst3: 3-class secondary structure labels (Q3)

Secondary Structure Labels

Q8 Labels (DSSP):

• H = Alpha helix
• E = Beta strand
• C = Coil/Loop
• G = 3-10 helix
• B = Beta bridge
• T = Turn
• S = Bend
• I = Pi helix

Q3 Labels:

• H = Helix (H, G, I)
• E = Strand (E, B)
• C = Coil (C, T, S)

Usage

`python from datasets import load_dataset

dataset = load_dataset("neuralninja110/dlgenai-nppe-dataset") `

Files

• train.csv: Training data with sequences and labels
• test.csv: Test data (sequences only)
• sample_submission.csv: Submission format template
• data/train.parquet: Training data in parquet format

Evaluation Metric

Harmonic mean of F1 scores for Q8 and Q3 predictions.

License

MIT License