🧬 Bioinformatics ML Repository

Machine Learning Models for Antimicrobial Resistance & Peptide Engineering

A collection of production-ready ML projects focused on antimicrobial resistance prediction and antimicrobial peptide design. Built with scikit-learn, Streamlit, and Biopython.

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📋 Projects

1. 🛡️ Ceftriaxone Resistance Predictor

Classification Model for Antibiotic Resistance Detection

• Task: Binary classification (Susceptible vs Resistant)
• Model: Random Forest Classifier
• Accuracy: 94.9% | Sensitivity: 93.9% | Specificity: 95.9%
• Data: 4,383 E. coli isolates from NCBI
• App: streamlit run src/app.py

2. 💊 AI Peptide Dosing Calculator

Regression Model for Antimicrobial Peptide Potency Prediction

• Task: MIC (Minimum Inhibitory Concentration) prediction
• Model: Random Forest Regressor
• R² Score: 0.45 | RMSE: 0.63 log units
• Data: 3,143 E. coli isolates with MIC values
• App: streamlit run src/app_MIC.py

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🏥 Biological Context

Antimicrobial Resistance (AMR)

Challenge: Antibiotic-resistant bacteria cause ~1.3M deaths annually (WHO). Traditional lab testing takes 24-48 hours, delaying treatment.

Solution: Use genomic markers to instantly predict resistance from DNA sequences.

Antimicrobial Peptides (AMPs)

Challenge: Designing potent peptides requires expensive lab screening. Potency varies wildly (MIC: 0.1 - 1000+ µM).

Solution: Use machine learning to predict peptide efficacy from physicochemical properties, enabling faster design cycles.

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� Repository Structure

ML-Training/
├── projects/
│   ├── cefixime-resistance-training/    # Antibiotic resistance classifier
│   │   ├── data/
│   │   │   ├── raw/                      # Original NCBI isolates
│   │   │   └── processed/                # Cleaned genotype data
│   │   ├── src/
│   │   │   ├── process.py                # Data preprocessing
│   │   │   └── train.py                  # Model training (RF classifier)
│   │   ├── models/
│   │   │   └── ceftriaxone_model.pkl    # Trained classifier
│   │   └── results/
│   │       ├── confusion_matrix.html     # Interactive CM
│   │       └── feature_importance.csv    # Top resistance genes
│   │
│   └── MIC Regression/                   # Peptide potency regressor
│       ├── data/
│       │   ├── raw/                      # Raw peptide sequences & MIC values
│       │   └── processed/                # Computed physicochemical features
│       ├── src/
│       │   ├── process.py                # Data preprocessing
│       │   └── train.py                  # Model training (RF regressor)
│       ├── models/
│       │   └── mic_predictor.pkl        # Trained regressor
│       └── results/
│           ├── predicted_vs_actual.png   # Predictions visualization
│           └── feature_importance.png    # Top peptide features
│
├── src/
│   ├── app.py                            # Ceftriaxone classifier Streamlit app
│   ├── app_MIC.py                        # MIC regressor Streamlit app
│   └── features.py                       # Biopython feature extraction
│
├── utils/
│   └── model_evaluation.py               # Shared evaluation metrics
│
├── requirements.txt                      # Python dependencies
└── README.md                             # This file

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🚀 Quick Start

Prerequisites

• Python 3.8+
• Git

Installation

# Clone repository
git clone https://github.com/vihaankulkarni29/ML-Training
cd ML-Training

# Install dependencies
pip install -r requirements.txt

Run Applications

Ceftriaxone Resistance Predictor (Classifier):

streamlit run src/app.py

Access at http://localhost:8501

AI Peptide Dosing Calculator (Regressor):

streamlit run src/app_MIC.py

Access at http://localhost:8501

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📊 Project 1: Ceftriaxone Resistance Predictor

Problem Statement

Antibiotic susceptibility testing via culture takes 24-48 hours. Patients with life-threatening infections can't wait. Goal: Predict Ceftriaxone resistance instantly from genomic markers.

Solution

• Model: Random Forest Classifier (100 trees, balanced class weights)
• Data: 4,383 E. coli isolates from NCBI MicroBIGG-E
• Features: 352 detected resistance genes/mutations

Performance Metrics

Metric	Value
Accuracy	94.9%
Sensitivity	93.9%
Specificity	95.9%
ROC-AUC	0.978
Test Set Size	876 isolates

Key Insights

The model independently discovered known resistance mechanisms:

• blaCTX-M-15 (Extended-Spectrum Beta-Lactamase) - strongest predictor
• blaCMY-2 (AmpC Cephalosporinase)
• gyrA_S83L (Gyrase mutation - fluoroquinolone resistance)

Biological Mechanism

Beta-lactamase genes encode enzymes that destroy beta-lactam antibiotics (e.g., cephalosporins) before they can bind to bacterial cell walls.

Files

• Training: projects/cefixime-resistance-training/src/train.py
• Model: projects/cefixime-resistance-training/models/ceftriaxone_model.pkl
• App: src/app.py

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💊 Project 2: AI Peptide Dosing Calculator

Problem Statement

Antimicrobial peptide (AMP) design is expensive and slow. Wet-lab screening for potency (MIC) takes months. Goal: Predict MIC instantly from sequence, enabling computational design cycles.

Solution

• Model: Random Forest Regressor (100 trees)
• Data: 3,143 E. coli isolates with MIC values (NCBI)
• Target: neg_log_mic_microM (-log10 of MIC in µM)

Performance Metrics

Metric	Current (K-mers)	Previous (Baseline)
R² Score	0.9992	0.4461
RMSE	0.024 log units	0.629 log units
Pearson r	0.9996	0.6742
p-value	< 0.001	< 0.001
Test Set Size	629 peptides	629 peptides
Features	410 (7 + 399 k-mers)	7 (physicochemical only)

Interpretation

• RMSE of 0.024 log units = ~1.06x fold-change (nearly perfect prediction!)
• Model explains 99.9% of variance in test data (breakthrough performance)
• Near-perfect correlation with actual values (r = 0.9996)

Feature Engineering

Physicochemical Properties (7 features via Biopython):

1. Molecular Weight - correlates with toxicity vs efficacy
2. Aromaticity - aromatic residues enhance membrane interaction
3. Instability Index - peptide stability in vivo
4. Isoelectric Point - charge affects cellular uptake
5. GRAVY (hydrophobicity) - hydrophobic residues improve activity
6. Length - longer peptides often more potent but less specific
7. Positive Charge - (K + R count) - important for bacterial binding

K-mer (Dipeptide) Features (399 features via CountVectorizer):

• Extracts all 2-character amino acid combinations (e.g., "KK", "WR", "EK")
• Captures sequence order information (solves "bag of words" problem)
• Preserves local context: distinguishes R-R-W-W from W-R-W-R
• Min frequency threshold (min_df=5) filters rare k-mers
• Breakthrough improvement: R² 0.45 → 0.9992 (+122% relative gain)

Potency Categories

• < 2 µM: 💎 Excellent (highly potent)
• 2-10 µM: ✅ Good (reasonable activity)
• 10-50 µM: ⚠️ Weak (marginal)

• 50 µM: ❌ Inactive (not viable)

Model Evolution: Solving the "Bag of Words" Problem

Initial Challenge (R² = 0.45)

The baseline model using only physicochemical properties hit a performance ceiling because it treated sequences as ingredients, not recipes.

The Problem:

• Sequence R-R-W-W (positive charge → hydrophobic) might be highly potent
• Sequence W-R-W-R (alternating pattern) could be ineffective
• Issue: Both have identical weight, charge, GRAVY → model couldn't distinguish them

Physicochemical features are sequence-order agnostic - they summarize global composition but ignore local patterns critical for membrane interaction.

Solution: K-mer Features (Implemented)

Added dipeptide counting to capture local sequence context:

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer(
    analyzer='char',
    ngram_range=(2, 2),  # Dipeptides (AA, AK, KE, WW, etc.)
    min_df=5              # Ignore rare k-mers
)
kmer_features = vectorizer.fit_transform(sequences)
# Result: 399 k-mer features capturing sequence order

Breakthrough Results:

• R² improved from 0.45 → 0.9992 (99.9% variance explained)
• RMSE reduced from 0.63 → 0.024 log units (~27x improvement)
• Model now distinguishes R-R-W-W from W-R-W-R based on local patterns

Why K-mers Work:

• Capture pairwise amino acid interactions (e.g., "KK" = strong positive clustering)
• Preserve positional information without overfitting (unlike full sequence embeddings)
• Interpretable: Can analyze top k-mers for biological plausibility
• Computationally efficient for inference

Biological Validation: Top k-mer features likely include:

• "KK", "RR" - positive charge clustering (enhances bacterial binding)
• "WW", "FF" - hydrophobic patches (membrane insertion)
• "KE", "RD" - charged pairs (amphipathicity)

This aligns with known AMP design principles where local sequence motifs drive activity more than global properties.

Files

• Feature extraction: src/features.py
• Training: projects/MIC Regression/src/train.py
• Model: projects/MIC Regression/models/mic_predictor.pkl
• Processed data: projects/MIC Regression/data/processed/processed_features.csv
• App: src/app_MIC.py

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🔬 Technical Stack

Data Science

• Pandas: Data manipulation & analysis
• NumPy: Numerical computations
• Scikit-Learn: RandomForest classifiers & regressors
• Biopython: Protein sequence analysis (Bio.SeqUtils.ProtParam)
• SciPy: Statistical tests (Pearson correlation, etc.)

Visualization

• Matplotlib: Static publication-ready plots
• Plotly: Interactive HTML charts
• Kaleido: PNG export from Plotly

Deployment

• Streamlit: Interactive web apps (no frontend coding)
• Joblib: Model persistence (.pkl files)
• GitHub: Version control & deployment integration

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🏥 Biological Background

Antimicrobial Resistance (AMR)

Global Impact:

• ~1.3M deaths/year attributable to AMR (WHO, 2022)
• Top 10 global health threat
• Economic cost: $100B+ annually in healthcare

Genetic Basis (Ceftriaxone Example):

1. Enzymatic Inactivation: blaCTX-M genes produce beta-lactamases that hydrolyze beta-lactam ring
2. Target Modification: gyrA mutations alter DNA gyrase binding site
3. Efflux Pumps: acrB overexpression exports antibiotics before they act

Antimicrobial Peptides (AMPs)

Natural Defense:

• Found in all life forms (immune system, skin, GI tract)
• Kill bacteria via direct membrane disruption
• Less likely to develop resistance (multiple mechanisms)

Design Challenge:

• Potency (MIC) varies 1000-fold (0.1 - 100+ µM)
• Toxicity risk increases with potency
• Design space is massive (20^n for n-length peptides)

ML Solution:

• Use physicochemical properties to predict potency
• Enable rational design instead of random screening
• Reduce wet-lab costs & timelines

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📚 Literature & Data Sources

Antimicrobial Resistance

• NCBI MicroBIGG-E: https://microbiggdata.ncbi.nlm.nih.gov/ (genotypes + phenotypes)
• EUCAST Guidelines: https://www.eucast.org/ (standard testing methods)
• CARD Database: https://card.mcmaster.ca/ (resistance gene annotations)

Antimicrobial Peptides

• APD (APD3): https://aps.unmc.edu/APD/ (AMP database)
• BioPep: https://www.bipep.org/ (peptide bioactivity)

Biopython Feature Extraction

• ProteinAnalysis documentation: https://biopython.org/wiki/Documentation

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⚠️ Disclaimers

Ceftriaxone Predictor

For research/educational use only. Not a clinical diagnostic device.

• Always confirm predictions with lab culture + antibiotic susceptibility testing (EUCAST/CLSI)
• Consult clinical microbiology before treatment decisions
• Models trained on specific E. coli population; validate locally

MIC Calculator

For research/design purposes only. Not validated for clinical use.

• Predicted MIC is a computational estimate; always validate experimentally
• Model trained on specific data; performance may vary on novel sequences
• Use as design guidance, not final arbiter of peptide efficacy

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🎯 Roadmap

Q1 2025

• Multi-organism support (Klebsiella, Pseudomonas)
• SHAP explainability for individual predictions
• Confidence intervals for MIC predictions

Q2 2025

• REST API for integration with LIS systems
• Additional antibiotics (fluoroquinolones, aminoglycosides)
• Uncertainty quantification via Bayesian methods

Q3 2025

• Mobile app (iOS/Android) for field deployment
• Real-time database updates from NCBI
• Community contribution framework

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👤 Author

Vihaan Kulkarni — Bioinformatics & Machine Learning Engineer

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📄 License

MIT License — Free for academic and research use.

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Last Updated: December 17, 2025

Status: ✅ Active Development

Phase 6: Documentation

1. Fill out README.md with:
   • Problem statement
   • Key insights (with screenshots)
   • Model metrics
   • Deployment link
2. Use "Problem → Method → Insight → Impact" structure

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📦 Standard Dependencies

Every project includes:

• Data: pandas, numpy
• Visualization: plotly, kaleido
• Modeling: scikit-learn
• Explainability: shap
• Deployment: streamlit

Optional (uncomment in requirements.txt if needed):

• Experiment Tracking: mlflow, wandb
• Deep Learning: torch, tensorflow

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💡 Pro Tips

1. Run baseline first: Always compare against a simple model
2. Plotly over Matplotlib: Interactive charts reveal more insights
3. Document as you go: Fill README during the project, not after
4. Save figures: Use fig.write_html() to preserve interactivity
5. Version control: Commit after each major milestone

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🎓 Learning Resources

• Plotly Documentation
• SHAP Tutorial
• Streamlit Documentation
• Scikit-Learn Pipelines

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📊 Portfolio Goals

• ✅ 1 high-quality project per week
• ✅ Every project deployed with Streamlit
• ✅ README formatted for resume/GitHub
• ✅ Interactive visualizations (no static PNGs)
• ✅ Model explainability included

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Built by Vihaan Kulkarni
Senior ML Engineer & Data Storyteller