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ar01-bioarrayexplorer

Microarray Dataset Analysis App offers metadata exploration, counts data analysis, differential expression analysis, and gene set enrichment analysis, providing a user-friendly interface and downloadable results for convenient analysis and sharing.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

bash-for-bioinformatics-

Hosting the Bash Files used in the course.

bioinformatics-data-analysis

This repository contains the course materials for the "Bioinformatics Data Analysis from Scratch" course, which covers the fundamentals of bioinformatics programming using Python, R, and Linux. The course is designed for beginners with no prior experience in bioinformatics, providing a comprehensive introduction to biological programming.

bioinformatics-gui-suite

Bioinformatics GUI Suite: Python Tkinter tools for alignment, variant calling, phylogenetic analysis, and genome annotations. Simplify bioinformatics tasks with user-friendly interfaces, offering both code and executable files for efficient data analysis.

bioinformatics-practical-bootcamp-

This GitHub repository contains all the metadata, codes, and pipelines used in the 'Complete Bioinformatics Practical Bootcamp from Zero to Hero' course.

gene-expression-rna-seq-pipeline-

The Pipeline is Designed to easily analyze the Raw data from sequencing Machines just use the pipeline code for quality checking to counting features

github-course

Hosting the Github Course Files from my Course

health-bioinformatics-course

heatmap-visulization-in-r

heatmap function in R Language is used to plot a heatmap. Heatmap is defined as a graphical representation of data using colors to visualize the value of the matrix.

meh

microarray-course

Hosting the source code of my microarray course on GitHub

portfolio.github.io

portfolio website

principal-component-analysis-pca-

Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Script of R to analyze the data of PCA.

python-course

Hosting the source code of my Python course on GitHub

r-course-source-code

Hosting the source code of my R course on GitHub

se7en69

Config files for my GitHub profile.