Machine-Learning-in-Bioinformatics-using-WEKA

Welcome to My Bioinformatics Project

This GitHub Page showcases:

• Feature extraction from protein sequences using R.
• Machine learning analysis on breast cancer dataset using Weka.

👉 Explore the full project below!

Protein Feature Extraction & Breast Cancer ML Analysis

This repository contains two main components related to bioinformatics and machine learning:

1. Protein Feature Extraction using R
2. Breast Cancer Classification using Machine Learning (Weka)

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📁 Files Included

• protein_feature_extraction.R: R script to extract multiple features from protein sequences using protr, Peptides, and foreign packages. Outputs are saved as .csv and .arff files for use in machine learning.
• JYOTHI_SWAROOP_ML_ASSIGNMENT.pdf: A detailed machine learning assignment using the Breast Cancer Wisconsin dataset analyzed through Weka.

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🔬 1. Protein Feature Extraction (R Script)

The R script performs the following:

• Reads protein sequences from FASTA files.
• Filters valid sequences using protcheck.
• Extracts various protein descriptors using:
  • extractDC, extractTC, extractCTDC, extractCTDD, extractCTDT, extractAPAAC, extractAAC
• Saves feature matrices for each descriptor in .csv format.
• Combines positive/negative classes and exports them as .arff files for ML tools like Weka.

Packages used:

• protr
• Peptides
• foreign

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🧠 2. Machine Learning Assignment (Weka)

The assignment explores classification models applied to the Breast Cancer Wisconsin dataset. The goal is to predict tumor recurrence based on morphological features.

🧪 Models Evaluated:

• IBk (k-Nearest Neighbors) – Best performer
• Naïve Bayes
• Support Vector Machine (SMO)

🔍 Key Findings:

| Model | Accuracy | Recall | F1-Score | ROC AUC | PRC AUC | |——-|———-|——–|———-|———|———| | IBk (k=1) | 72.37% | 0.724 | 0.697 | 0.628 | 0.686 | | Naïve Bayes | 71.67% | 0.717 | 0.708 | 0.701 | 0.741 | | SMO (SVM) | 69.58% | 0.696 | 0.600 | 0.509 | 0.586 |

• Best overall: IBk (k-NN), especially for recall—critical in medical diagnosis.
• Naïve Bayes handled noise and missing values well.
• SVM underperformed due to data characteristics and model assumptions.

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🧬 Biological Relevance

• Machine learning helps predict recurrence risk, aiding early diagnosis and personalized treatment.
• Protein features extracted can be applied to similar predictive modeling tasks.
• Bioinformatics + ML provides a powerful toolset for computational biology.

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✅ How to Use

For R Script:

1. Ensure required R packages are installed: protr, Peptides, foreign.
2. Update file paths in the script if needed.
3. Run the script to generate .csv and .arff files for your sequences.

For Weka Assignment:

1. Open Weka GUI.
2. Load the .arff files.
3. Apply classifiers under the “Classify” tab.
4. Evaluate results using 10-fold cross-validation.

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📌 Author

Jyothi Swaroop C
Machine Learning in Bioinformatics – Assignment & R-based Feature Extraction

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📬 Contact

Feel free to reach out if you have any questions or want to collaborate!

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