Our latest IntelliGenes article published in the Biology Methods & Protocols (Oxford) i.e.,
Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life, however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: 1) Data Manager, 2) AI/ML Analysis, and 3) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer reviewed studies, was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for non-technical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.
IntelliGenes, Project Publications:
- Narayanan, R., Degroat, W., Mendhe, D., Abdelhalim, H., & Ahmed, Z.* (2024). IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine. Collection: Artificial Intelligence in Biology and Bioinformatics. Biology Methods & Protocols. 9(1), bpae040. PMID: 38884000. (Oxford).
- 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. (Oxford).