Artificial Neural Network Technology (ANN)
The company’s Artificial Intelligence ANN technology has enabled “in silico” research to be undertaken across a wide range of projects. Disease indications being addressed include prostate cancer, lung cancer, pancreatic cancer, breast cancer, acute myeloid leukaemia, tuberculosis (human and bovine), sepsis, diabetes, neurodegenerative diseases and cardiovascular disease. Intelligent OMICS has specific and extensive experience in the analysis of gene expression array data, mass spectrometry data, exon array data, flow cytometry data and questionnaire data. However the methodologies available are applicable to any large high dimensional data set.
The technology used is based on over 20 years of research undertaken by Professor Graham Ball and colleagues at Nottingham Trent University.
A Novel Approach for Drug & Biomarker Discovery
Intelligent OMICS has developed a cutting edge systems biology and bioinformatics approach, based on computational intelligence, which identifies robust non-linear biomarkers associated with clinical features. So if you need to find a new molecular target for the disease you are studying, or a companion diagnostic that will reduce the failure risk in your clinical trial then the Intelligent OMICS system has the answer.
Since the sequencing of the human genome, new approaches for studying disease systems at the genomic, epigenetic, proteomic and metabolomics levels are being continually developed. The twin challenges with the analysis of such data are the volume, resolution and complexity of the data generated and the issue of data quality. In the past, analysis of different data sets for a given clinical question yielded different results. We now know that any such inconsistency is driven either by the level of ‘noise’ in the data or by shortfalls in the number of cases, resulting in insufficient statistical power. We also now know that analysis of biological data requires acceptance of the non-linearity of biological systems, the interaction of molecular entities within pathways, and the fluidity of biological systems.
Intelligent OMICS has developed its systems biology and bioinformatics approach based on artificial (computational) intelligence which identifies robust non-linear biomarkers associated with clinical features which are shown to be consistently important across multiple data sets. The methodology studies interactions between key features, determining the identity of important biomarkers and the level of influence of a set of driver markers in a given biological system.
Based on our patented technology we have developed an approach that addresses the challenges of “Big Data” whilst ensuring that results are relevant and validated in terms of each question under study. Intelligent OMICS determines the molecular drivers of a system that govern phenotype.