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+# IntelliGenes
+
+This is the CLI implementation of _IntelliGenes_. A GUI version is available in the intelligenes-gui branch. 
+
+IntelliGenes is a Python-based portable pipeline that addresses challenges arising from the cascading volume of genomics datasets being created that require interpretation. IntelliGenes serves as a comprehensive toolkit, fitting cutting-edge algorithms for discovering disease-associated biomarkers and patient prediction to users’ unique cohorts. IntelliGenes integrates demographics with genomics, facilitating investigations that consider both variables simultaneously. With IntelliGenes, we introduce I-Genes Scores, our novel metric for understanding the relevance of biomarkers in disease prediction engines.
+
+_IntelliGenes_ can be installed through our GitHub using the terminal. Follow the provided steps to install IntelliGenes and the package’s dependencies: 
+```
+# Clone IntelliGenes’ GitHub Repository
+git clone https://github.com/drzeeshanahmed/intelligenes.git
+
+# Navigate to IntelliGenes
+cd intelligenes/
+
+# Install IntelliGenes
+pip install .
+```
+
+_IntelliGenes_ offers a robust selection of tools to help users understand their multi-genomics datasets. _IntelliGenes_ has been designed as an easy-to-understand pipeline for those at all levels of computational understanding. _IntelliGenes_ has three functions:
+```
+# Discover Biomarkers
+igenes_select -i data/cigt_file.csv -o results/
+
+# Disease Prediction & I-Genes Scores 
+igenes_predict -i data/cigt_file.csv -f features_file.csv -o results/
+
+# IntelliGenes (Discovering Biomarkers & Predicting Disease) 
+igenes -i data/cigt_file.csv -o results/
+```
+
+These are sample commands. We have provided an example CIGT file in tests/.
+
+These commands all users to write various flags that will tailor _IntelliGenes_ to their exact needs: 
+```
+# IntelliGenes Selection Help
+igenes_select --help
+
+# IntelliGenes Prediction Help
+igenes_predict --help
+
+# IntelliGenes Help
+igenes --help
+```
+
+_IntelliGenes_ requires a CIGT formatted dataset as an input. Examples of CIGT datasets can be found on our GitHub. The CIGT formatted dataset integrates demographics and transcriptomic: 
+  -	Columns contain demographic or transcriptomic biomarkers, while rows contain identifiers for individual patients. 
+  -	Demographics such as ‘Age’, ‘Race’, and ‘Sex’ should be integers (use EHR standards). These demographics are not required, as IntelliGenes works using only genomics/transcriptomics.
+  -	There must be a ‘Type’ column, denoting a patient’s status as an integer (use 0 or 1). 
+
+More information is available in **Supplementary Material 2: _IntelliGenes_: Installation, configuration, and user’s guidelines**
+
+If using _IntelliGenes_, please cite: 
+
+Degroat, W., Mendhe, D., Bhurasi, A., Abdelhalim, H., Saman, Z., & Ahmed, Z. (2023). IntelliGenes: A novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics. 39, 12. btad755. PMID: 38096588. doi:10.1093/bioinformatics/btad755 (Oxford).