Metadata-Version: 2.4
Name: senseqnet
Version: 1.1.2
Summary: A Deep Learning Framework for Cellular Senescence Detection from Protein Sequences
Home-page: https://github.com/HanliJiang13/SenSeqNet_Package
Author: Hanli Jiang
Author-email: hhanlijiang@mail.utoronto.ca
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: torch>=1.10.0
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: biopython
Requires-Dist: click
Requires-Dist: fair-esm
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
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# SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection from Protein Sequences

This repository includes source code, datasets, and models for the study "SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection from Protein Sequences".


---

## Overview

**SenSeqNet** is a deep learning framework designed to detect cellular senescence from protein sequences. By leveraging advanced protein language models (e.g., ESM2) for embedding extraction and a specialized LSTM+CNN architecture, SenSeqNet aims to provide a **fast** and **accurate** way to predict whether a given protein sequence is associated with senescence.

## Features

- **ESM-based Embeddings**: Automatically extract high-quality embeddings using ESM2.
- **BiLSTM+CNN Architecture**: Combines the strengths of recurrent and convolutional layers for effective feature learning.
- **Command-Line Interface (CLI)**: Quickly run predictions on your own FASTA files using a single command.
- **PyPI Package**: Easily install the package and integrate it into your workflow.

---

## Installation

SenSeqNet is **available on PyPI**. You can install it with:

```bash
pip install senseqnet
```
---


## Usage

Once installed, the CLI tool senseqnet-predict is available.
Below is a simple example using CUDA:

```bash
senseqnet-predict --fasta test_sequences.fasta --device cuda
```

Command-Line Arguments 
--fasta: Path to your input FASTA file (required).
--device: Inference device (e.g., "cpu" or "cuda"). Defaults to "cuda" if available.

Google Colab sample notebook: https://colab.research.google.com/drive/1e9lMOIIvppdpAYRy7LUYUPh2xpzpp8cr?usp=sharing


## Contact
For inquiries, please contact Hanli Jiang at: hhanli.jiang@mail.utoronto.ca
