proteo-suite-json9112 0.1.13

Advanced Proteomics Analysis Suite for Medical Outcomes Prediction

Proteo Suite

Advanced Proteomics Analysis Suite for Medical Outcomes Prediction

proteo_suite is a Python library designed for advanced mass spectrometry signal processing and machine learning analysis to predict medical outcomes from proteomics data.

Features

• Advanced Signal Processing: Wavelet transforms, spectral analysis, peak enhancement.
• Machine Learning: Ensembles (XGBoost, LightGBM, CatBoost), Deep Learning (Transformers, CNNs).
• Survival Analysis: Kaplan-Meier, risk stratification.
• Statistical Validation: Bootstrap confidence intervals, calibration analysis, comprehensive reporting.

Installation

Option 1: Install from PyPI (Recommended)

pip install proteo-suite-json9112

Option 2: Install from Private GitHub (Alternative)

pip install git+https://github.com/YOUR_USERNAME/proteo_suite.git

Option 2: Install Locally

For development or local use:

pip install .

Usage

Enhanced Pipeline

from proteo_suite import EnhancedProteomicsPipeline

# Initialize
pipeline = EnhancedProteomicsPipeline()

# Run analysis
results = pipeline.run_analysis(
    data_file="path/to/data.csv",
    outcome_columns=['Pneumonitis', 'Grade_3_or_above']
)

Comprehensive Validation

from proteo_suite import ComprehensiveValidator

validator = ComprehensiveValidator()
validator.validate_model(y_true, y_scores, model_name="MyModel")

Advanced Analyzer (Full Stack)

from proteo_suite import AdvancedProteomicsAnalyzer

analyzer = AdvancedProteomicsAnalyzer(data_path="data.csv")
analyzer.run_complete_analysis()

Dependencies

• numpy, pandas, scipy, scikit-learn
• xgboost, lightgbm, catboost
• lifelines
• torch (optional, for deep learning)
• matplotlib, seaborn