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🤖 ML Project
Genomic Mutation Analysis with Hybrid ML Models
Hybrid machine learning model for understanding human genomic dynamics and mutation patterns with comprehensive EDA and predictive modeling.
Project Overview
Research Project
Bioinformatics / Genomics / Healthcare
3 months (2024)
ML Engineer / Data Scientist
This research project applies hybrid machine learning models to understand human genomic dynamics and mutation patterns. The analysis includes comprehensive exploratory data analysis (EDA) to understand genomic data distributions, feature engineering for genetic sequences, and a hybrid model combining multiple ML algorithms to predict mutation patterns and their potential impacts on human health.
The Challenge
Understanding human genomic mutations requires analyzing complex, high-dimensional genetic data with multiple interacting factors:
High-dimensional genomic data - Thousands of genetic features with complex interactions
Imbalanced mutation classes - Rare mutations underrepresented in datasets
Complex feature relationships - Non-linear interactions between genetic markers
Interpretability requirements - Medical applications need explainable predictions
Data quality issues - Missing values and noise in genetic sequencing data
Computational complexity - Large-scale genomic datasets require efficient processing
Validation challenges - Need for rigorous cross-validation and biological validation
Required a comprehensive analytical approach with thorough EDA, robust feature engineering, and a hybrid model architecture to capture complex genomic patterns.
My Solution
I developed a hybrid machine learning pipeline with extensive exploratory analysis and multiple modeling approaches:
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Exploratory Data Analysis
Comprehensive EDA including distribution analysis, correlation heatmaps, mutation frequency visualization, and statistical significance testing.
Feature Engineering
Genomic feature extraction, sequence encoding, dimensionality reduction with PCA, and feature selection using mutual information.
Hybrid Model Architecture
Ensemble approach combining Random Forest, Gradient Boosting, and Neural Networks for robust mutation prediction.
Model Interpretation
SHAP analysis for feature importance, partial dependence plots, and biological pathway mapping.
Key Features
Genomic EDA
Comprehensive exploratory analysis of mutation patterns, frequencies, and genomic distributions.
Statistical Analysis
Hypothesis testing, correlation analysis, and significance testing for genetic markers.
Hybrid Model
Ensemble of Random Forest, XGBoost, and Neural Networks for robust predictions.
Mutation Prediction
Predict mutation likelihood and potential pathogenicity scores.
Visualization
Interactive plots for genomic distributions, feature importance, and model performance.
Interpretable AI
SHAP values and feature importance for explainable genetic insights.
Tech Stack
Data Analysis
Visualization
ML Framework
Bioinformatics
Environment
Screenshots
Exploratory Data Analysis - Genomic Data Distribution and Mutation Patterns
Correlation Heatmap - Feature Relationships and Genetic Marker Interactions
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Hybrid Model Training - Ensemble Architecture and Performance Metrics
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SHAP Analysis - Feature Importance and Mutation Predictors
Results & Impact
92%
Accuracy
Mutation Prediction
0.89
AUC-ROC
Score
1000+
Features
Analyzed
3
Models
Ensemble
85%
Precision
Pathogenic
50K+
Samples
Processed
Key Achievements
Achieved 92% accuracy in mutation classification with hybrid ensemble model
Identified top 20 genomic features most predictive of pathogenic mutations
Reduced false positive rate by 35% compared to single-model approaches
Comprehensive EDA revealed novel patterns in mutation frequency distribution
SHAP analysis provided interpretable insights for biological validation
Processed 50,000+ genomic samples with optimized computational pipeline
Cross-validated results aligned with known biological pathways
Open-source Colab notebook enables reproducible research
Client Testimonial
“This genomic analysis project demonstrates the power of combining rigorous exploratory analysis with hybrid machine learning. The interpretable results provide valuable insights for understanding mutation dynamics in human genomics.”
Research Collaboration
Bioinformatics Research, Academic Project
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