World's first Cardiovascular specific whole genome analysis service, Resource for Life

根据世界卫生组织的数据，31%的死亡是由心血管疾病（CVD）造成的，其中高达80%的死亡可以通过向高危人群提供重要的预防信息来改变生活方式（KTN Horizon Toolkit）来避免。该方法评估了19个单核苷酸多态性（snp）的DNA，以提供心血管疾病风险的个性化报告。我们的愿景是使用全基因组测序和机器学习技术彻底改变心血管遗传学测试过程。这将对32亿个碱基对数据进行测序，存储个人的整个基因组，并部署机器学习以数字方式提供医生所需的多种报告。这些将每天自动更新，并在直观访问的安全在线门户中立即可用。目前的检测过程需要四种不同的患者检测，每种检测费用为250英镑，总共需要6-10周的时间。这对临床医生来说是不方便的，对医院来说是昂贵的，对病人来说是不舒服的。它将以更低的成本转化为一次测试，这将大大提高可靠性。*研究、开发和评估应用于全基因组的随机森林机器学习过程的概念验证，以评估心血管疾病风险。*与专家心脏病专家评估报告界面*评估已开发的生命资源平台的交付效率主要关注领域是引领行业研究*准确性-应用机器学习算法提高多基因风险评分准确性*基础设施-数据库和流程*接口-强调GDPR知情同意创新源于*更高的准确性-随机森林机器学习生成模型*商业模式-资源生命报告中心将自动向医生和患者提供报告*完整的数据集，用于生命-所有32亿个碱基对的全基因组测序现在和永远是所有信息，在一次测试中。

According to The World Health Organisation, 31% of all deaths are due to CardioVascular Disease (CVD), up to 80% of which can be avoided by supplying people at risk with vital preventative information to change lifestyle (KTN Horizon Toolkit)Storegene currently provides a preventative genetics service. This assesses DNA across 19 Single Nucleotide Polymorphisms (SNPs) to provide personalised reports on CVD risk.Our vision is to revolutionise this cardiovascular genetics test process using whole genome sequencing and machine learning techniques. This will sequence 3.2 billion base pairs of data, storing an individual's whole genome, and deploying machine learning to digitally provide multiple reports required by physicians. These will automatically be updated every day and available instantly in an intuitively accessible secure online portal.This will combine four specific reports using a single genome test to coverThe current process requires four different patient tests each at a cost of £250 taking a combined circa 6-10 weeks turnaround. This is inconvenient for the clinician, costly for the hospital and uncomfortable for the patient. It will be transformed using a lower cost all in one test that is significantly more reliable.Key objectives are* To research, develop and evaluate in a proof of concept the random forest machine learning process applied to whole genomes to assess CVD risk.* To evaluate the reports interface with an expert Cardiologist* To assess efficiency of delivery of the developed Resource for Life platformMain areas of focus are to lead industrial research in* Accuracy -- applying machine learning algorithms to improve polygenic risk score accuracy* Infrastructure -- database and processes* Interfaces -- emphasis on GDPR informed consentInnovation arises through* More accuracy - random forest machine learning to generate model* Business model - the Resource for LIfe report centre will automatically provide reports to physicians and patients* Complete dataset, for life - whole genome sequencing for all 3.2 Billion base pairs is all information now and forever, in one test.