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Daxiao123/
LucaVirusCress

Name: LucaVirusCress
Availability: InStock

pytorch

bert

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LucaVirusCress: A Deep Learning Framework for CRESS Virus Identification

License: MIT

Python

LucaVirusCress is a specialized binary classification model for identifying CRESS (Circular Rep-Encoding Single-Stranded) DNA viruses by targeting REP (Replication-associated protein). Built on LucaProt, it supports high-throughput viral sequence screening in large metagenomic datasets.

Project Overview
Training Performance
Dataset Details
Technical Specifications
Usage
Model Weights
License

Project Overview

Task: Binary classification (CRESS REP vs. non-CRESS REP)
Target: CRESS virus REP proteins
Architecture: SequenceAndStructureFusionNetwork (SEFN), a 4-layer Transformer-based fusion network
Application: Discovery of novel CRESS viruses from environmental and clinical omics data

Training Performance

The model reached optimal convergence at 4,000 global steps.

The training process used weighted loss to handle extreme class imbalance.

Metric	Set	Visualization
Confusion Matrix	Test	Test confusion matrix
Confusion Matrix	Dev	Dev confusion matrix
F1 Score	Evaluation	F1 score
Training Loss	Steps	Training loss

Dataset Details

To ensure high specificity, the model was trained on a highly imbalanced dataset (1:40 ratio) with carefully selected hard negatives.

Positive samples: Verified CRESS REP protein sequences
Negative samples:
1. Non-CRESS proteins with high sequence similarity to REP
2. Proteins with structural motifs similar to the target class
3. Non-target proteins in the CRESS viral genome (for example, Cap proteins)

Technical Specifications

Resource	Specification
Hardware	NVIDIA L40S (46 GB VRAM)
Compute	16 vCPUs
Software	CUDA 12.8 / Driver 570.172.08
Training Time	Approximately 20 hours

Usage

1. Environment Setup

bash

git clone https://github.com/lpyhhh/LucaVirusCress.git
cd LucaVirusCress

conda env create -f environment.yaml
conda activate lucaviruscress

# Download model files from Hugging Face
git clone https://huggingface.co/Daxiao123/LucaVirusCress hf_model

# Copy model files into checkpoint directory
mkdir -p models/cress/protein/binary_class/sefn/20230201140320/checkpoint-4000
cp -r hf_model/* models/cress/protein/binary_class/sefn/20230201140320/checkpoint-4000/

# Clean temporary directory
rm -rf hf_model

2. Single Sequence Prediction

The embedding matrix is generated in real time.

bash

cd src

python predict_one_sample.py \
  --protein_id protein_1 \
  --sequence "MTTSTAFT...(your sequence)" \
  --emb_dir ../emb \
  --dataset_name cress \
  --dataset_type protein \
  --task_type binary_class \
  --model_type sefn \
  --time_str 20260321140320 \
  --step 4000 \
  --threshold 0.5 \
  --gpu_id 0

3. Batch Prediction (FASTA)

For large-scale screening of metagenomic assemblies:

bash

cd src

python predict_many_samples.py \
  --fasta_file ../data/input.fasta \
  --save_file ../result/prediction_results.csv \
  --emb_dir ../emb \
  --dataset_name cress \
  --model_type sefn \
  --time_str 20260321140320 \
  --step 4000 \
  --gpu_id 0

Key Parameters

--truncation_seq_length: Maximum sequence length (default: 4096)
--pos_weight: Handled internally (configured as 40 in config.json for imbalance)
--threshold: Classification threshold (default: 0.5)

Model Weights

Pre-trained weights and configuration are hosted on Hugging Face:

https://huggingface.co/Daxiao123/LucaVirusCress

License

This project is licensed under the MIT License. See LICENSE for details.

Acknowledgment

Thanks to everyone who supported this project.

Daxiao123/LucaVirusCress

LucaVirusCress: A Deep Learning Framework for CRESS Virus Identification

Contents

Project Overview

Training Performance

Dataset Details

Technical Specifications

Usage

1. Environment Setup

2. Single Sequence Prediction

3. Batch Prediction (FASTA)

Key Parameters

Model Weights

License

Acknowledgment

Daxiao123/
LucaVirusCress