HPClas: A new computational approach for identifying halophilic proteins based on Machine Learning

Introduction

Code details

• Users can run the HPClas.py to identify the Halophilic proteins.

• featureGenerator.py is implemented for feature generation.

The features descriptors including Amino Acid Composition (AAC), the Composition of K-Spaced Amino Acid Pairs (CKSAAP), Di-Peptide Composition (DPC), Tri-Peptide composition (TPC), and Dipeptide Deviation from Expected Mean (DDE),the Composition(CTDC), Transition(CTDT), Distribution(CTDD), Conjoint Triad (CTriad), Grouped amino acid composition(GAAC), Grouped Di-Peptide Composition(GDPC).

• predicton.py can be used for the final prediction based on the generated features.

Dependency

• python 3.9
• catboost 1.0.6
• scikit_learn 1.2.1

Dataset

• train_P.fasta: postives samples for training
• train_N.fasta: negative samples for training
• test_P.fasta: postives samples for testing
• test_N.fasta: negative samples for testing

**The customized file should be consistant with the example files(input/example.fasta)

Installation Guide

• Install from Github

git clone https://github.com/Showmake2/HPClas
cd HPClas

Usage

• Run the default dataset

python HPClas.py --type benchmark

• Make the prediction for customize data and threshold

python HPClas.py --type predict --fasta_file <Fasta_file>  --output_name <output_name> --threshold <threshold>

• output results format | |mean|pred| | :-----------: | :-----------: | :-----------: | |index|pred_prob|final score|