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Parkinson's Disease Protein Sequence Classifier

Overview

A machine learning pipeline for classifying protein sequences associated with Parkinson's disease using various sequence features and multiple classification models. The project achieves 80.3% accuracy using LSTM architecture with comprehensive sequence feature analysis.

Table of Contents

Installation and Dependencies

Prerequisites

  • Python 3.11+
  • CUDA capable GPU (optional, for faster training)

Quick Start

git clone https://github.com/Spritan/ParkinsonDiseaseClassifier
cd ParkinsonDiseaseClassifier
pip install uv
uv run main.py

Dependencies

dependencies = [
    "biopython>=1.84",
    "matplotlib>=3.9.2",
    "pandas>=2.2.3",
    "rich>=13.9.4",
    "scikit-learn>=1.5.2",
    "seaborn>=0.13.2",
    "torch>=2.5.1",
    "xgboost>=2.1.2",
]

Dataset

Data Format (FASTA)

FASTA files contain protein sequences in a text-based format:

>sp|Q9Y6M9|NDUB9_HUMAN NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 9
MAFLASGPYLTHQQKVLRLYKRALRHLESWCVQRDKYRYFACLMRARFEEHKNEKDMAKA
TQLLKEAEEEFWYRQHPQPYIFPDSPGGTSYERYDCYKVPEWCLDDWHPSEKAMYPDYFA
KREQWKKLRRESWEREVKQLQEETPPGGPLTEALPPARKEGDLPPLWWYIVTRPRERPM
  • Header line (starts with '>'): Contains sequence identifier and metadata
  • Subsequent lines: Amino acid sequence in single-letter code

Data Sources

  1. Control Sequences (129 samples)

    • Source: data/cleaned_output (1).fasta
    • Average length: 482 amino acids
    • Contains various human proteins without PD association

  2. Parkinson's Disease Sequences (165 samples)

    • Source: data/uniprotkb_parkinson_disease_protein_AND_2024_11_15.fasta
    • Average length: 523 amino acids
    • Proteins associated with PD pathology

Sequence Analysis

1. Sequence Properties Distribution

Sequence Properties

  • Length Distribution: Right-skewed, ranging from 100 to 2000+ amino acids
  • GC Content: Normal distribution centered around 0.52
  • Hydrophobic Ratio: Peaks at 0.40, indicating typical protein composition
  • Charged Ratio: Centered at 0.25, showing consistent charge distribution

2. Amino Acid Composition

Amino Acid Distribution

  • Most Abundant:
    • Leucine (L): ~10%
    • Alanine (A): ~7.8%
    • Serine (S): ~7.2%
  • Least Abundant:
    • Tryptophan (W): ~1.4%
    • Cysteine (C): ~2.2%
    • Histidine (H): ~2.4%
  • Class Differences:
    • Slight variations in Serine (S) content between healthy and PD
    • Minor differences in charged amino acids (R, K, D, E)

3. K-mer Patterns

2-mer Frequency 3-mer Frequency

Bi-mer Analysis:

Top 5 Most Frequent:
1. LE (7.2%): Leucine-Glutamic acid
2. AL (6.8%): Alanine-Leucine
3. LA (6.5%): Leucine-Alanine
4. EL (5.9%): Glutamic acid-Leucine
5. SE (5.7%): Serine-Glutamic acid

Tri-mer Analysis:

Top 5 Most Frequent:
1. LEE (3.1%): Leucine-Glutamic acid-Glutamic acid
2. ALA (2.9%): Alanine-Leucine-Alanine
3. LAA (2.8%): Leucine-Alanine-Alanine
4. ELL (2.7%): Glutamic acid-Leucine-Leucine
5. AAL (2.6%): Alanine-Alanine-Leucine

4. Feature Correlations

Feature Correlations

  • Strong correlations between related k-mers
  • Moderate correlations between physicochemical properties
  • Weak correlations between different feature types

Data Quality

  • No missing values
  • Valid amino acid sequences (20 standard amino acids)
  • Balanced class distribution (43.88% healthy, 56.12% PD)
  • Consistent sequence format and annotation

Preprocessing Steps

  1. Sequence validation
  2. Length normalization
  3. Feature extraction
  4. Feature scaling
  5. Train-test split (80-20)

Pipeline Architecture

1. Data Processing

def load_fasta_data(healthy_path, parkinsons_path):
    """
    Load and process FASTA sequences
    Returns DataFrame with sequences and labels
    """
  • FASTA file parsing using BioPython
  • Sequence validation and preprocessing
  • Data frame construction with sequence metadata

2. Feature Engineering (More details later on ...)

Features extracted include:

  • K-mer frequencies (k=2,3)
  • Basic sequence properties:
    • Length
    • GC content
    • Hydrophobic ratio
    • Charged amino acid ratio
  • Total features extracted: 7,950
  • Features selected for modeling: 50

3. Model Training

Multiple models evaluated:

  • Deep Learning:
    • LSTM
    • Neural Network
  • Traditional ML:
    • SVM
    • Random Forest
    • Gradient Boosting
    • XGBoost
    • KNN

Feature Engineering and Selection

Feature Extraction Pipeline

The feature extraction process is handled by the SequenceFeatureExtractor class (reference: src/feature_extraction.py, lines 9-48), which implements a comprehensive approach to protein sequence analysis:

class SequenceFeatureExtractor:
    def __init__(self, k=3):
        self.k = k
        self.vectorizer = CountVectorizer(analyzer='char', ngram_range=(k, k))
        
    def compute_basic_features(self, sequence):
        return {
            'length': len(sequence),
            'gc_content': (sequence.count('G') + sequence.count('C')) / len(sequence),
            'hydrophobic_ratio': sum(aa in 'AILMFWYV' for aa in sequence) / len(sequence),
            'charged_ratio': sum(aa in 'DEKR' for aa in sequence) / len(sequence)
        }

Feature Categories

1. Sequence-based Features

  • Length: Raw sequence length
  • GC Content: Proportion of Glycine and Cytosine
  • Hydrophobic Ratio: Proportion of hydrophobic amino acids (A, I, L, M, F, W, Y, V)
  • Charged Ratio: Proportion of charged amino acids (D, E, K, R)

2. K-mer Features

The system uses CountVectorizer to generate:

  • Bi-mers (k=2): Captures local sequence patterns
  • Tri-mers (k=3): Captures extended sequence motifs

Feature Selection Process

Feature selection is implemented in SequenceDataAnalyzer (reference: src/data_analysis.py, lines 81-97) using a multi-method approach:

def select_features(self, n_features=50):
    """
    Select top features using ensemble of methods:
    1. Random Forest importance
    2. ANOVA F-scores
    3. Mutual Information
    """

Selection Methods

  1. Random Forest Importance

    • Evaluates feature importance through decision trees
    • Particularly effective for capturing non-linear relationships
    • Robust to feature scaling

  2. ANOVA F-scores

    • Statistical test for feature relevance
    • Identifies features with significant class separation
    • Assumes normal distribution

  3. Mutual Information

    • Information theory-based approach
    • Captures non-linear relationships
    • Scale-invariant feature selection

Feature Selection Workflow

  1. Initial Feature Space

    • Total features extracted: 7,950
    • Includes all k-mers and basic properties
    • Sparse matrix representation for efficiency

  2. Feature Ranking

    • Each method ranks features independently
    • Scores are normalized to [0,1] range
    • Combined ranking through weighted voting

  3. Feature Reduction

    • Final selection: Top 50 features
    • Balanced representation across feature types
    • Optimized for model performance

Feature Importance Analysis

The system provides comprehensive feature importance visualization through multiple plots:

  1. Correlation Analysis
startLine: 103
endLine: 127
  1. Feature Importance Plotting
startLine: 128
endLine: 159

Performance Impact

Feature selection significantly improves model performance:

  1. Computational Efficiency

    • 99.4% reduction in feature space
    • Faster training times
    • Reduced memory requirements

  2. Model Performance

    • Improved generalization
    • Reduced overfitting
    • More interpretable models

  3. Cross-validation Results

    • Selected features maintain 80.3% accuracy
    • Stable performance across folds
    • Robust to different model architectures

Future Enhancements

  1. Advanced Feature Engineering

    • Position-specific scoring matrices
    • Secondary structure predictions
    • Evolutionary conservation scores
    • Domain-specific features

  2. Selection Methods

    • Deep learning-based feature selection
    • Ensemble selection strategies
    • Dynamic feature importance tracking

  3. Optimization

    • Feature selection hyperparameter tuning
    • Custom feature importance metrics
    • Real-time feature importance updates

Results

Detailed Model Performance Analysis

1. LSTM (Best Model)

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ Metric    ┃ Score (mean ± std) ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ Accuracy  │      0.803 ± 0.046 │
│ Precision │      0.816 ± 0.078 │
│ Recall    │      0.845 ± 0.095 │
│ F1        │      0.825 ± 0.054 │
└───────────┴────────────────────┘
  • Best overall performance
  • Balanced precision and recall
  • Consistent performance across classes

2. Neural Network

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ Metric    ┃ Score (mean ± std) ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ Accuracy  │      0.718 ± 0.028 │
│ Precision │      0.718 ± 0.065 │
│ Recall    │      0.830 ± 0.094 │
│ F1        │      0.764 ± 0.042 │
└───────────┴────────────────────┘
  • Second-best performer
  • High recall but lower precision
  • More stable metrics (lower std)

3. Traditional ML Models Comparison

Model              F1-Score        Accuracy
----------------------------------------
Gradient Boosting  0.714 ± 0.048  0.656 ± 0.040
Random Forest      0.690 ± 0.043  0.639 ± 0.036
XGBoost           0.686 ± 0.040  0.636 ± 0.044
KNN               0.683 ± 0.034  0.595 ± 0.039

Feature Analysis Results

1. Amino Acid Distribution

Amino Acid Distribution

  • Leucine (L): ~10% frequency in both classes
  • Key differences in Serine (S) and Alanine (A)
  • Rare amino acids: Cysteine (C), Tryptophan (W)

2. K-mer Analysis

Most significant patterns:

Bi-mers:  LE (7.2%), AL (6.8%), LA (6.5%)
Tri-mers: LEE (3.1%), ALA (2.9%), LAA (2.8%)

3. Feature Importance Rankings

Top features by method:

  1. Random Forest:
    • Hydrophobic ratio
    • Length
    • GC content
  2. ANOVA F-score:
    • K-mer patterns
    • Charged ratio
  3. Mutual Information:
    • Sequence length
    • K-mer frequencies

Performance Visualization

Model Comparison

  • LSTM consistently outperforms other models
  • Deep learning models show superior performance
  • Traditional ML models cluster around 65-70% accuracy

Technical Implementation Details

Core Components

  1. Data Processing Pipeline:
def process_sequences(sequences):
    """
    Process raw sequences:
    1. Validate amino acid content
    2. Extract features
    3. Normalize values
    """
  1. Feature Extraction:
class SequenceFeatureExtractor:
    """
    Extract features from protein sequences:
    - K-mer frequencies
    - Sequence properties
    - Physicochemical properties
    """
  1. Model Training:
class SequenceClassifier:
    """
    Train and evaluate multiple models:
    - Deep learning (LSTM, NN)
    - Traditional ML
    """

Performance Optimization

  1. Feature Selection Process:
  • Initial features: 7,950
  • Selected features: 50
  • Selection methods:
    • Random Forest importance
    • ANOVA F-scores
    • Mutual Information
  1. Cross-validation Strategy:
  • 5-fold stratified CV
  • Consistent random seed
  • Performance metrics with std

Future Work

1. Dataset Enhancement

  • Target: 1000+ sequences per class
  • Include:
    • More control sequences
    • Various PD subtypes
    • Related neurodegenerative diseases

2. Feature Engineering

  • Position-specific scoring matrices
  • Secondary structure predictions
  • Domain-specific features
  • Evolutionary conservation scores

3. Model Improvements

  • Attention mechanisms
  • Transformer architectures
  • Ensemble strategies
  • Model interpretability

4. Pipeline Optimization

  • Model persistence
  • Parallel processing
  • Configuration management
  • API development

Acknowledgments

  • UniProt database for protein sequences
  • BioPython community
  • Rich library developers
  • PyTorch team