Research is at the core of Intelligent OMICS activities. The technology used is based on over 20 years of research undertaken by Professor Graham Ball and colleagues at Nottingham Trent University.
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.
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Intelligent OMICS undertakes and manages its research in one of three ways:
The company collaborates with Academia, Charities, NHS, Research Establishments and Industry
Collaborative projects which have used the Intelligent OMICS technology include:
- Bovine TB- Intelligent OMICS led an Innovate (UK) Funded project to develop a field diagnostic device for Bovine TB. Partners were Public Health England, Sapient Sensors Ltd, CPI Innovations Services with specialist support being given by AFBI in Northern Ireland and Nottingham. Trent University. The project identified an optimised set of markers suitable for a protein/peptide POC device that had the ability to identify cattle in an early stage of infection and set of biomarkers that can delineate disease severity and, likely risk of increased infection transmission. IP from the project is in the course of being patented and a suitable device being developed.
- SPAG 5- Development of Cancer markers for response to therapy and markers core to proliferation- i.e. Personalised Medicine –Proliferation markers and profiles have been recommended for guiding the choice of systemic treatments for breast cancer. The Intelligent OMICS tools were therefore used, in a study by the J Van Geest Cancer Research Centre and the City Hospital Nottingham, to identify factors that drive proliferation and its associated features in breast cancer and assess their association with clinical outcomes and response to chemotherapy.
Analysis was undertaken independently across multiple data sets to identify common proliferation markers. The analysis identified 34 genes consistently associated with multiple proliferation-related features in breast cancer – and found SPAG5 to be the most influential and that when it is amplified in specific subtypes of breast cancer the gene and its associated proteins are linked to better outcome for patients in response to anthracycline based chemotherapy, 60 % of patients are more likely to respond to treatment with anthracycline. This in silico study by Professor Graham Balls was validated by Histopathology techniques and full results published in Lancet Oncology June 13:2016, Tarek Abdel-Fatah and Colleagues. The results formed the basis of a successful NIRH i4i application to develop and trial a diagnostic test.
- Tomato Ripening- In collaboration with Syngenta and the University of Nottingham the Intelligent OMICS technology was used to examine the drivers of ripening in tomatoes. Ripening mutants of tomatoes were profiled using expression array technologies. The resultant was mined using the company’s ANN technology and a rank order of biomarkers was identified. The top 500 markers were run through the Network Inference algorithm and this identified three key highly influential Transcription Factors (TF) were identified. When a transgenic plant was created, if these TF were suppressed the tomatoes ripened in third of the time, if these TFs were stimulated the tomatoes did not ripen. A patent has been granted based on these results.
Contract research is undertaken on a fee-for-service basis for customers, and is subject to confidentiality agreements.
Currently contract research is being carried out in the following fields:
- COPD – Intelligent OMICS used its patented technology to analysis time series COPD data using 10 measured biomarkers in order to predict exacerbation before they occur. The results of the analysis provided information for an optimised panel of markers that predict exacerbation. From this work Intelligent OMICS will develop a computer based Decision Support Model utilising the results of the ANN analysis in the form of Java script or similar.
- Cancer – Our client, a biotech company, worked with us to source and format suitable RNA Sequence Data, quantify the variation and mode of action of a candidate enzyme across different cancers and determining the “Interactome” of the enzyme across selected cancer data sets.
RNA sequence data was analysed in order to determine the rank order of Transcripts based on their proportional contribution of splicing to the parent gene.
We were also able to identify key molecular drivers using our Network Inference methodology in a cell line system.
Further validation studies were undertaken to validate results by using data in publicly available repositories for Acute Myeloid Leukaemia patients.
- Sepsis – The company has completed a contract with DSTL to conduct combination analysis of microarray and qPCR pre-symptomatic data. Subsequently Intelligent OMICS has been awarded a further contract to analysis a variety of sepsis and other infectious disease data. We believe this demonstrates the reliability and benefits of the Intelligent OMICS technology.
Intelligent OMICS undertakes research on its own account. The company selects suitable publicly available data sets and then analysing these to answer questions of clinical and commercial relevance. The company seeks to liaise with relevant ”experts” to ensure the validity of the questions being posed. Current projects include:
- Mycobacterium Tuberculosis ( Human TB) Using a publicly available gene expression dataset (Berry et al.) the Intelligent OMICS technology was used to derive an optimal gene transcript signature discriminating between healthy cases, latent TB and active TB in a UK cohort(n=120). Final results included:
A four Gene Panel discriminating between Latent TB and healthy individuals
A diagnostic model with 99% sensitivity and 74% specificity, much better than original results with just a panel of four biomarkers compared with 293 in the original study, differentiating between healthy individuals and those with latent TB
- COPD – For this research project we have collected three public datasets containing RNA sequencing data from COPD patients. Two were based on sputum and one on blood. The focus of this project is to identify progression markers that will identify the state and likelihood of progression from one disease stage tto the next. Therefore each stage and each stage combination was analysed using the Intelligent OMICS tools. The concordance of each feature across stages was determined and the information used with our network inference algorithm to predict pairwise gene interactions.
The novel markers identified with these techniques are unique not only to each stage but to each stage transition allowing for the construction of a marker panel indicative of COPD progression at each stage.
We are now in discussions with Clinicians to discuss how the results can be best used to help manage the care of those with COPD