https://github.com/thearpankumar/adaptation-gene-prediction
Machine Learning model that can predict whether a gene from the bacterium Deinococcus radiodurans helps it survive extreme stress (like radiation)
https://github.com/thearpankumar/adaptation-gene-prediction
Last synced: 11 months ago
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Machine Learning model that can predict whether a gene from the bacterium Deinococcus radiodurans helps it survive extreme stress (like radiation)
- Host: GitHub
- URL: https://github.com/thearpankumar/adaptation-gene-prediction
- Owner: thearpankumar
- License: apache-2.0
- Created: 2025-06-17T06:23:04.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-06-17T06:31:17.000Z (about 1 year ago)
- Last Synced: 2025-06-17T07:29:16.457Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 19.5 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://colab.research.google.com/drive/1jQbwFb-HLqWTv7pHwN9mDyGylIdVTp4w?usp=sharing)
# Machine Learning for Adaptation Gene Prediction
This project uses machine learning to predict whether a gene from the bacterium *Deinococcus radiodurans* contributes to stress tolerance based on features derived purely from its DNA sequence. The goal is to create a computational tool that can rapidly screen genomes for candidate stress-response genes, potentially aiding in bioengineering and synthetic biology.
## Table of Contents
- [Project Goal](#project-goal)
- [How It Works](#how-it-works)
- [Features Engineered](#features-engineered)
- [Machine Learning Pipeline](#machine-learning-pipeline)
- [How to Use This Project](#how-to-use-this-project)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Running the Notebook](#running-the-notebook)
- [File Descriptions](#file-descriptions)
- [Example Prediction Workflow](#example-prediction-workflow)
- [Future Work](#future-work)
## Project Goal
The primary objective is to build and train a machine learning model that can classify a given gene as either a "stress-response gene" or a "normal/housekeeping gene." Instead of relying on expensive and time-consuming laboratory experiments, this model leverages patterns in the DNA sequence itself to make predictions.
This serves as a proof-of-concept for a high-throughput screening tool to prioritize genes for further study in newly sequenced organisms, especially extremophiles.
## How It Works
The project follows a classic supervised learning approach:
1. **Data Collection:** The complete annotated genome of *Deinococcus radiodurans* (`.gbff` format) is downloaded from the NCBI database.
2. **Labeling:** Genes are assigned one of two labels:
* **`Stress` (Positive Class):** A list is created using a hybrid strategy of (a) manually curated, literature-verified stress genes and (b) programmatic searching for functional keywords like "DNA repair," "radiation resistance," "chaperone," etc.
* **`Control` (Negative Class):** A list of housekeeping genes is created using a similar strategy, identifying genes for essential functions like "ribosomal protein" or "gyrase."
3. **Feature Engineering:** Each gene's DNA sequence is converted into a set of meaningful numerical features that the model can understand.
4. **Model Training:** An XGBoost classifier is trained on the labeled, feature-engineered dataset to learn the patterns that differentiate the two classes.
## Features Engineered
The model uses a rich set of features to build its predictions:
- **Basic Sequence Properties:**
- `Gene Length`: Total length in base pairs.
- `GC Content`: Percentage of Guanine and Cytosine bases.
- `GC3 Content`: GC content at the third position of codons.
- `CG Dinucleotide Frequency`: The relative abundance of CG pairs.
- **Protein-Level Features:**
- `Hydrophobicity (GRAVY)`: The Grand Average of Hydropathicity of the translated protein.
- `Isoelectric Point`: The pH at which the translated protein has no net charge.
- **Pattern-Based Features:**
- `Motif Frequencies`: Occurrence of known regulatory motifs.
- `K-mer Frequencies`: A high-resolution sequence "fingerprint" based on the frequency of all possible 4-letter DNA substrings (e.g., 'AAGC', 'GATA').
## Machine Learning Pipeline
A robust pipeline ensures the model is trained correctly and its performance is reliable:
1. **Preprocessing (`VarianceThreshold`):** Automatically removes useless features that are constant across all samples.
2. **Feature Selection (`SelectKBest`):** Selects the top 100 most informative features using a statistical ANOVA F-test, reducing noise and complexity.
3. **Handling Class Imbalance (`SMOTE`):** Artificially balances the training data by creating synthetic examples of the rare "Stress" class, preventing the model from becoming biased.
4. **Hyperparameter Tuning (`GridSearchCV`):** Systematically tests different model configurations (e.g., `learning_rate`, `max_depth`) to find the optimal settings.
5. **Training (`XGBoost`):** The final model is an XGBoost classifier, a powerful gradient boosting algorithm well-suited for tabular data.
## How to Use This Project
### Prerequisites
- Python 3.8+
- Jupyter Notebook or JupyterLab
### Installation
1. Clone this repository to your local machine:
```bash
git clone
cd
```
2. Install the required Python libraries using pip:
```bash
pip install biopython scikit-learn pandas xgboost matplotlib imblearn joblib
```
### Running the Notebook
1. Launch Jupyter Notebook or JupyterLab:
```bash
jupyter notebook
```
2. Open the main notebook file (e.g., `gene_prediction_pipeline.ipynb`).
3. Execute the cells in order from top to bottom. The notebook is self-contained and will automatically:
- Download the necessary genome data.
- Perform all data processing and training steps.
- Save the final model and all required pipeline components.
- Demonstrate how to load the saved artifacts and make predictions on sample data.
## File Descriptions
```
.
├── gene_prediction_pipeline.ipynb # Main Jupyter Notebook with all the code.
├── README.md # This README file.
└── GCF_000012145.1_ASM1214v1_genomic.gbff # Genome data (downloaded by the notebook).
└── saved_artifacts/
├── stress_gene_model.joblib # The final, trained XGBoost model.
├── feature_selector.joblib # The fitted SelectKBest object.
├── variance_thresholder.joblib # The fitted VarianceThreshold object.
├── kmer_vectorizer.joblib # The fitted CountVectorizer for k-mers.
└── feature_columns.joblib # The list of feature names the model expects.
```
## Example Prediction Workflow
After running the main notebook once, you can use the saved artifacts to predict on a new DNA sequence:
1. **Load Artifacts:** Load the model, selector, thresholder, vectorizer, and column list using `joblib`.
2. **Process New Sequence:** Apply the exact same feature engineering steps to your new sequence.
3. **Align Features:** Use `.reindex()` to ensure the new feature vector has the same columns in the same order as the training data.
4. **Apply Preprocessors:** Transform the data using the loaded `variance_thresholder` and `feature_selector`.
5. **Predict:** Use `loaded_model.predict()` to get the final classification.
An example of this entire workflow is provided in the final section of the main Jupyter Notebook.
## Future Work
- **Expand the Dataset:** Incorporate data from other extremophiles (e.g., tardigrades, thermophiles) to create a more general and robust model.
- **Explore More Features:** Engineer additional features, such as codon adaptation index (CAI) or predicted protein secondary structure.
- **Try Different Models:** Experiment with other algorithms like LightGBM or deep learning models (e.g., Convolutional Neural Networks) to compare performance.
- **Deploy as a Web App:** Package the model and prediction pipeline into a simple web application where users can paste a DNA sequence and get a prediction.