Identification of disease related biomarkers in time varying ‘Omic data: A non-negative matrix factorization aided multi level self organizing map based approach

Abstract

The development of disease detection with progression modelling due to long-term exposure to toxicity is complex. This leads to unwrapping the underlying intricate molecular network of toxicity and is thus a crucial challenge for the researchers. Therefore, identifying a set of biomarkers to predict the risk of exposure is vital. Thus, this article aims to provide a holistic machine learning-based solution over various time-varying ‘omic data to understand and explore the factors involved in the development of diseases. To address this issue, a flexible non-negative matrix factorization based multi-level self organizing map (FNMF-MLSOM) is developed. The proposed algorithm utilizes two open-source time series datasets: Type-2 diabetes mellitus and Huntington disease, namely. The flexible non-negative matrix factorization based self organization model introduced in this article provides a negative value acceptance constraint as well as the clustering on the basis matrix to keep the biological meaning of the data intact. Since microarray data have rich information, we applied the proposed method to obtain the progression-specific convoluted biomarker for precise feature extraction. Further, to validate the differentially expressed biomarkers, the proposed method is applied to the test samples to verify the mathematical validity as well as the biological significance of the biomarkers.