Metadata-Version: 2.1
Name: OncoMark
Version: 1.0.0
Summary: A deep learning tool designed to predict Cancer Hallmark activities from tumor biopsy samples.
Home-page: https://github.com/SML-CompBio/OncoMark
Author: Shreyansh Priyadarshi
Author-email: shreyansh.priyadarshi02@gmail.com
License: Apache-2.0 license
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas ==2.2.3
Requires-Dist: numpy ==2.0.2
Requires-Dist: tensorflow ==2.18.0
Requires-Dist: joblib ==1.4.2
Requires-Dist: scipy ==1.14.1

# OncoMark

**OncoMark** is a Python package designed to systematically quantify hallmark activity using transcriptomics data from routine tumor biopsies. Ideal for applications in oncology research, personalized medicine, and biomarker discovery.

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## Installation

Install OncoMark using pip:

```bash
pip install OncoMark
```

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## Documentation

Comprehensive documentation is available at:  
[OncoMark Documentation](https://oncomark.readthedocs.io/en/latest/)

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## Usage

### Python API

```python
import pandas as pd
from OncoMark import predict_hallmark_scores

# Load input data as a pandas DataFrame. Genes must be in column.
input_data = pd.read_csv('input_data.csv', index_col=0)

# Predict hallmark scores
predictions = predict_hallmark_scores(input_data)

# Display the predictions
predictions
```

### Web Server

OncoMark also provides a web server for easy interaction.

#### Access the Online Web Server

You can use the hosted web server directly:

[OncoMark Web Server](https://iamspriyadarshi-oncomark.hf.space/)

## Suggestions

We welcome suggestions! If you have any ideas or feedback to improve OncoMark, please reach out to [Shreyansh Priyadarshi](mailto:shreyansh.priyadarshi02@gmail.com).

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