OncoFinder maps the gene expression data onto known and custom intracellular signaling pathways. These pathways regulate numerous processes involved in normal and pathological conditions including development, growth, aging, and cancer. Analysis of these signaling pathways makes it possible to measure differences in physiological state such as aging, disease, or chemical exposure among individual samples using microarray data. The difference in gene expression among signaling pathways can be used to predict which drugs and other interventions will be most effective for improving health. For example, OncoFinder can be used by researchers to analyze the effectiveness of drugs for cancer treatment or to determine pathways involved in age-related diseases.

OncoFinder is distinguished by its unique algorithm that quantifies perturbations for each signaling pathway. This makes it possible to quantitatively estimate the extent of each signaling pathway activation in a given sample relative to the control sample or a set of control samples. The algorithm separates the positive/activator and negative/repressor role of each gene in the pathway to determine the role of each gene in signal transduction and calculate the pathway activation strength. The pathway activation strength measures the degree of functional changes in the regulation of a signaling pathway, with positive or negative values indicating if the pathway is up- or down-regulated, respectively. Using the pathway activation strength, OncoFinder scores drugs based on their involvement in each signaling pathway and the ability of the drug to restore healthy gene expression.

OncoFinder's biomathematical method can be used for both quantitative and qualitative analysis of intracellular signaling pathway activation (SPA). This method is universal and may be used for the analysis of any physiological, stress, malignancy, or other perturbed condition at the molecular level. In contrast to other existing techniques for aggregation and generalization of gene expression data from individual samples, we suggest to distinguish the positive/activator and negative/repressor role of every gene product in each pathway. We show that the relative importance of each gene product in a pathway can be assessed using kinetic models for "low-level" protein interactions.

Due to its universal applicability, the method OncoFinder, published in the beginning of March 2014, is already widely used by the researcher community globally.

The OncoFinder platform enables researchers to calculate the Drug Score index that makes it possible to range the existing anticancer drugs according to their potential for to selectively kill the cancer cells of an individual patient. Each study performed by the OncoFinder team is personalized, since anticancer drug analysis is based strictly on the patient-specific gene expression data.

Method based on analyzing the expression of over 15,000 protein-coding genes and microRNA sequences to construct a map of the intracellular signaling pathways affected by an individual patient's cancer.

Our method is based on personalized profiling of malignant tissue transcriptomes versus a complementary set of healthy, normally functioning tissues, where the transcriptome is defined as the entire set of transcribed RNA molecules, which either transmit genetic information from DNA (genes) to functional proteins (mRNAs) or influence activity of protein-encoding mRNAs (microRNAs).

Although knowing aberrations in certain genes commonly mutated in cancer may be helpful to study familial disorders or to roughly predict sensitivity of certain cancers to targeted therapies, molecular profiling of the activity of the entire genome (i.e. transcriptome) is much more comprehensive and informative and has been established to have a far superior predictive value in the modern molecular medicine compared to the analysis if individual genetic alterations.

This may be partly explained by the fact that there are two copies of DNA in the human genome, meaning that active mutations or substitutions in one of the copies may be masked by the other, also active, yet non-mutated copy of DNA. The protein produced from the non-mutated gene sequence can sometimes mask the abnormal protein produced from an almost identical, yet mutated gene sequence.