Intelligent SME.tech Issue 14 | Page 45

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As part of the EU-funded iCOPE research project into childhood cancer , Jesper Vang and Adrian Otamendi – both PhD Students in the Department of Health Technology , Cancer Systems Biology at the Technical University of Denmark ( DTU ) – are using TigerGraph ’ s advanced graph analytics and Machine Learning to help improve the treatment of acute lymphoblastic leukaemia by analysing genetic factors which may affect patients ’ clinical evolution and responses to treatments .
Research in cancer genomics using Whole Genome Sequencing ( WGS ) generates vast amounts of data which is currently stored in a MySQL relational database . However , the DTU researchers said the information is simply too much for humans to analyse in what is effectively an enormous spreadsheet , making it nearly impossible to correlate data on treatments and outcomes . What they needed was a solution that would present the data visually and intuitively while also enabling clinicians to query the data themselves without having to become experts in SQL data queries .
Now TigerGraph ’ s advanced graph analytics with Machine Learning and AI has enabled the researchers to view the data in a new way . By layering the graph database on top of the MySQL database , they are able to create links between genetic data and patient data and use graph visualisation tools to graphically represent the data surrounding illnesses , diagnoses , treatments and outcomes . And using the graph query language and Machine Learning , they are also able to identify previously hidden correlations in the data .
Jesper Vang and Adrian Otamendi told IntelligentSME . tech how TigerGraph will help them analyse these complex datasets to develop a better understanding of treatment outcomes and toxicities .
Can you tell us more about the work carried out at the Technical University of Denmark ? of next-generation sequencing data from Danish children with cancer . Whole genome sequencing ( WGS ) will be performed to analyse germline mutations and RNA sequencing will be used to characterise the somatic variation in 600 Danish children with cancer . Associations between germline and tumour mutations will be analysed as well as their association to clinical biomarkers and treatment outcomes .
This study will investigate genetic variation in children with cancer who suffered drug-related side effects from an approved treatment in Denmark to find correlations between individual genetics and treatment outcomes , side effects and toxicities .
How will using TigerGraph provide earlier diagnosis and more effective treatment ?
The study will focus on side effects that are well characterised in medical journals and have registered pharmacogenomic flags or associations to genetic biomarkers . We will use data mining on the current literature and select genetic and clinical biomarkers that show strong evidence and association with the treatment response and toxicity .
Genetic variation and clinical data on these biomarkers for the 600 patients will be extracted and input into TigerGraph . Connections between genetic variants and treatment outcomes and toxicities found by
Previous page : Adrian Otamendi and Jesper Vang , PhD Students in the Department of Health Technology , Cancer Systems Biology at the Technical University of Denmark

WHAT THEY NEEDED WAS A SOLUTION THAT WOULD PRESENT THE DATA VISUALLY AND INTUITIVELY .
At iCOPE at DTU , we aim to provide the germline and somatic mutational landscape for childhood cancer through the analysis
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