Machine learning and data mining in complex genomic data a review on the lessons learned in Genetic Analysis Workshop Nineteen

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets

Machine Learning and Data Mining in Complex Genomic Data - A Review on the Lessons Learned in Genetic Analysis Workshop 19

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data. In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets

Patient-Reported Outcomes in a Randomized Trial of Closed-Loop Control: The Pivotal International Diabetes Closed-Loop Trial

International audienceBackground: Closed-loop control (CLC) has been shown to improve glucose time in range and other glucose metrics; however, randomized trials >3 months comparing CLC with sensor-augmented pump (SAP) therapy are limited. We recently reported glucose control outcomes from the 6-month international Diabetes Closed-Loop (iDCL) trial; we now report patient-reported outcomes (PROs) in this iDCL trial. Methods: Participants were randomized 2:1 to CLC (N = 112) versus SAP (N = 56) and completed questionnaires, including Hypoglycemia Fear Survey, Diabetes Distress Scale (DDS), Hypoglycemia Awareness, Hypoglycemia Confidence, Hyperglycemia Avoidance, and Positive Expectancies of CLC (INSPIRE) at baseline, 3, and 6 months. CLC participants also completed Diabetes Technology Expectations and Acceptance and System Usability Scale (SUS). Results: The Hypoglycemia Fear Survey Behavior subscale improved significantly after 6 months of CLC compared with SAP. DDS did not differ except for powerless subscale scores, which worsened at 3 months in SAP. Whereas Hypoglycemia Awareness and Hyperglycemia Avoidance did not differ between groups, CLC participants showed a tendency toward improved confidence in managing hypoglycemia. The INSPIRE questionnaire showed favorable scores in the CLC group for teens and parents, with a similar trend for adults. At baseline and 6 months, CLC participants had high positive expectations for the device with Diabetes Technology Acceptance and SUS showing high benefit and low burden scores. Conclusion: CLC improved some PROs compared with SAP. Participants reported high benefit and low burden with CLC. Clinical Trial Identifier: NCT03563313