Omics-Cnn: A Comprehensive Pipeline for Predictive Analytics in Quantitative Omics Using One Dimensional Convolutional Neural Networks

Background and Objective: The development of machine learning-based models that can be used for the prediction of severe diseases has been one of the main concerns of the scientific community. The current study seeks to expand a highly sophisticated tool, the Convolutional Neural Networks, making it applicable to multidimensional omics data classification problems and test the newly introduced method on publicly available transcriptomics and proteomics data.Methods: In this study, we introduce Omics-CNN, a Convolutional Neural Network-based pipeline, which couples Convolutional Neural Networks with dimensionality reduction, preprocessing, clustering, and explainability techniques to make them suitable to build highly accurate and interpretable classification models from high-throughput omics data. The developed tool has the potential to classify patients depending on the expression of genetic and clinical factors and identify features that can act as diagnostic biomarkers. Regarding dimensionality reduction, univariate and multivariate techniques were explored and compared. Gradient Weighted Class Activation Mapping analysis was performed to determine the most important features in the classification of the samples after training the model.Results: The newly introduced pipeline was applied to one transcriptomics and one proteomics dataset for the identification of diagnostic models and biosignatures for Ischemic Stroke and Covid-19 infection, reporting highly accurate biosignatures of Area Under the Curve of 95.38% and 98.49%, respectively. Meanwhile, classification models based solely on a small part of attributes provided lower predictive accuracy, but identified a compact transcript biosignature (KRT15, VPRBP, TNFRSF4, GORASP2 ) for Ischemic Stroke and a protein biosignature (ADGRB3, VNN2, AGER, CIAPIN1) for Covid-19 infection diagnosis, respectively.Conclusions: Omics-CNN, overcame the inherent problems of applying Convolutional Neural Networks for the training diagnostic models with quantitative omics data, outperforming previous models of machine learning developed using the same datasets for Ischemic Stroke and Covid-19 infection diagnosis, determining the most contributing biomarkers for both diseases.