Intelligent SME.tech Issue 14 | Page 46

// END-USER INSIGHT //

THESE CAN BE VERY SMALL BUT
SIGNIFICANT
FEATURES THAT WOULD BE MISSED BY THE GENERAL
STATISTICAL ANALYSIS
BECAUSE THEY MIGHT ONLY
APPLY TO FEWER THAN 2 % OF
PATIENTS .
TigerGraph will help us categorise individuals into meaningful risk and outcome-based pharmacogenetic ( PGx ) groups .
The aim is to create a tool that allows the exploration of the genetic variation in the PGx biomarkers across our patient groups and the assessment of individual risk and outcome to different drugs to finally predict risk-grouping based on individual genetic and clinical data .
Where does the data come from ?
STAGING ( sequencing of Tumor and Germline DNA – Implications and National Guidelines ) is a research project that offers whole genome sequencing to all children and young people diagnosed with cancer under the age of 18 in Denmark . The project started on July 1 , 2016 and is thus the first project that does extensive sequencing of all patients in a medical speciality . As such , around 600 children diagnosed with cancer have already been sequenced .
Why is TigerGraph easier to work with than the previous system ?
We try to find associations and connections across our data . For example , patients who present some specific variant or variant set respond worse to some specific treatments . In the current scenario , this relationship or connection is hidden under the entire dataset and only the specific queries will allow us to discover it .
However , TigerGraph could help point to the connections or associations in our data by integrating different data sources and enabling a visual exploration of relationships .
How does it combine AI , Machine Learning and translational bioinformatics ?
We will use clinical treatment-related data along with the individual genetic variation data to create a model that , with Machine Learning and AI , will allow us to extract meaningful connections or associations between genomics and treatment outcomes and predict potential toxicities in different groups of patients .
How have you found the new system since it came online ?
It ’ s early days using TigerGraph , but it will help us extract the relationships between datapoints in large and complex datasets . After all , we are looking for correlations between genetics and treatment outcome by analysing 600 genomes of children with cancer . These can be very small but significant features that would be missed by the general statistical analysis because they might only apply to fewer than 2 % of patients .
Graph analytics will allow us to find new correlations between patients ’ treatments , outcomes and genetic variations . It will enable us to answer questions that were difficult to formulate before but can be answered now thanks to the explicit relationship between datapoints inherent in the structure of the TigerGraph database and the power of graph analytics and Machine Learning . �
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