Репозиторий Университета

© Springer Science+Business Media, LLC, part of Springer Nature 2020. We describe here the Oncobox method for scoring efficiencies of anticancer target drugs (ATDs) using high throughput gene expression data. The method rationale, design, and validation are given along with the examples of its practical applications in biomedicine. The method is based on the analysis of intracellular molecular pathways activation and measuring expressions of molecular target genes for every ATD under consideration. Using Oncobox method requires collection of normal (control) expression profiles and annotated databases of molecular pathways and drug target genes. Both microarray and RNA sequencing profiles are acceptable, although the latter type of data prevails in the most recent applications of this technique.

© Springer Science+Business Media, LLC, part of Springer Nature 2020. Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.