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comparative_ml_analysis_bioinformatics

A comprehensive analysis of gene expression data using machine learning techniques in Python and R, focusing on predictive modeling and data visualization

https://github.com/mojo8787/comparative_ml_analysis_bioinformatics

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (10.7%) to scientific vocabulary

Keywords

bioinformatics data-visualization gene-expression machine-learning random-forest regression-analysis svr
Last synced: 6 months ago · JSON representation ·

Repository

A comprehensive analysis of gene expression data using machine learning techniques in Python and R, focusing on predictive modeling and data visualization

Basic Info
  • Host: GitHub
  • Owner: mojo8787
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 1.89 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
bioinformatics data-visualization gene-expression machine-learning random-forest regression-analysis svr
Created about 2 years ago · Last pushed 7 months ago
Metadata Files
Readme Citation

README.md

Comparative Machine Learning Analysis in Bioinformatics

DOI

GitHub release License: MIT Contributors ORCID Made with R

Introduction

This project focuses on comparative machine learning analysis in the field of bioinformatics, specifically examining gene expression data. The analysis involves various machine learning techniques, including Random Forest, Support Vector Regression (SVR), and other regression models, to predict and analyze gene expression scores.

Technologies and Libraries Used

  • Python: Used for data preprocessing, model building, and evaluation.
    • Key Libraries: pandas, numpy, sklearn, seaborn, matplotlib
  • R: Employed for statistical analysis and visualization.
    • Key Libraries: tidyverse, caret, e1071, rpart, randomForest, ggplot2, readr, ggpubr

Data Description

The project uses preprocessed gene expression data, including various features and a target variable (score). The data is analyzed to understand the relationships between different genes and their expression levels.

Machine Learning Models and Techniques

  • Random Forest Regression (Python): Used for hyperparameter tuning and model fitting.
  • Support Vector Regression (SVR) (Python & R): Applied for modeling gene expression data with linear kernel.
  • Feature Selection and Analysis: Mutual Information, Recursive Feature Elimination (RFE), and Correlation Analysis.
  • Model Evaluation: Using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
  • Baseline Comparison: Comparison with a dummy regressor to establish baseline performance.

Visualizations

Key visualizations from the analysis are presented below:

Line Plot

Line Plot Description of the line plot.

Scatter Plot

Scatter Plot Explanation of the scatter plot findings.

Bar Chart

Bar Chart Details about the data shown in the bar chart.

Density Plot

Density Plot Interpretation of the density plot.

Joint Density Plot

Joint Density Plot Insights from the joint density plot.

Results and Discussion

Summary of key findings, including feature importance, model performance comparison, and visualization insights.

Installation and Setup

Instructions on setting up the environment and running the scripts.

Usage

Details on how to run the scripts and utilize the analysis.

Contributing

Information on how others can contribute to the project.

Contact

For more information or inquiries, please contact motasem.youniss@gmail.com.

Owner

  • Login: mojo8787
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it using the metadata below."

# Basic metadata
title: "Comparative Machine Learning Analysis in Bioinformatics"
version: "1.0.0"
date-released: "2025-07-28"

# DOI will be automatically updated by Zenodo upon the first release tag
# Replace XXXXXXX with the actual Zenodo record number once generated.
doi: 10.5281/zenodo.16539671

# Authors
authors:
  - family-names: "Younis"
    given-names: "Motasem"
    affiliation: "Independent Researcher"
    orcid: "https://orcid.org/0000-0003-2070-2811"

# Repository URL
repository-code: "https://github.com/USERNAME/Comparative_ML_Analysis_Bioinformatics"

# License
license: "MIT"

# Keywords (optional; helps indexing)
keywords:
  - machine learning
  - bioinformatics
  - random forest
  - support vector regression
  - kernel methods

GitHub Events

Total
  • Release event: 1
  • Push event: 3
Last Year
  • Release event: 1
  • Push event: 3

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 9
  • Total Committers: 2
  • Avg Commits per committer: 4.5
  • Development Distribution Score (DDS): 0.222
Past Year
  • Commits: 9
  • Committers: 2
  • Avg Commits per committer: 4.5
  • Development Distribution Score (DDS): 0.222
Top Committers
Name Email Commits
mojo8787 m****s@g****m 7
Motasem younis 9****7 2

Issues and Pull Requests

Last synced: about 2 years ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Dependencies

.github/workflows/python-package-conda.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
.github/workflows/r.yml actions
  • actions/checkout v3 composite
  • r-lib/actions/setup-r f57f1301a053485946083d7a45022b278929a78a composite