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[Recorded Webinar and Slides Are Available Now!] Join dkNET Webinar: Multi-Omics Data Integration for Phenotype Prediction of Type-1 Diabetes
*Watch recorded webinar here: https://youtu.be/qCP09Bf-xfk
*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-multiomics-data-integration-for-phenotype-prediction-of-type1-diabetes-04092021
Join dkNET Webinar on Friday, April 9, 2021, 11 am - 12 pm PDT
Abstract
Omics techniques (e.g., i.e., transcriptomics, genomics, and epigenomics) report quantitative measures of more than tens of thousands of biological features and provide a more comprehensive molecular perspective of studied diabetes mechanisms compared to transitional approaches. Identifying representative molecular signatures from the tremendous number of biological features becomes a central problem in utilizing the data for clinical decision-making. Exploring the complex causal relations of the identified representative molecular signatures and diabetes phenotypes can be the most effective and efficient ways to improve the understanding of diabetes and assess the cause of diabetes for the new patients with already collected data influencing (e.g., TEDDY project). However, due to the unavoidable patient heterogeneity, statistical randomness, and experimental noise in the high-dimension, low-sample-size omics data of the diabetic patients, utilizing the available data for clinical decision-making remains an ongoing challenge for many researchers. To overcome the limitations, in this study we developed (1) a generative adversarial network (GAN)-based model to generate synthetic omics data for the samples with few omics profiles available; (2) a deep learning-based fusion network model for phenotype prediction of type-1 diabetes; (3) a long short-term memory (LSTM)-based model for predicting outcomes of islet autoantibody and persistent positivity. The models are tested on the multi-omics data in TEDDY project.Presenter: Wei Zhang, Ph.D. Assistant Professor, Department of Computer Science & Genomics and Bioinformatics Cluster, University of Central Florida
Dial-in Information: https://uchealth.zoom.us/meeting/register/tZwrcO6vrTksG9VzsopYGFEfcnsdFhOkHnWW
Upcoming webinars schedule: https://dknet.org/about/webinar
About
The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant 2U24DK097771-09.
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