Integrative Omics & Biomedical Informatics Laboratory

Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA

© 2016 Dokyoon Kim, All Rights Reserved

Human-Disease Phenotype Map

This is the phenotype connectivity map from one of the largest PheWAS using electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger’s MyCode Community Health Initiative genotyped through the DiscovEHR project. Click on each disease node to highlight other diseases found to be associated with this disease via SNPs.

Anurag Verma, Lisa Bang, Jason E. Miller, Yanfei Zhang, Ming Ta Michael Lee, David J. Carey, Marylyn D. Ritchie, Sarah A. Pendergrass, Dokyoon Kim, on behalf of the DiscovEHR collaboration, Human-disease phenotype map derived from PheWAS across 38,682 individuals, American Journal of Human Genetics, 2019 [PubMed]

hdpm.biomedinfolab.com

MildInt: Deep learning-based multimodal longitudinal data integration framework

The python package MildInt (Deep learning-based Multimodal longitudinal data integration framework) provides the pre-constructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and deep learning model higher accuracy and better interpretability can be achieved. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset.

https://github.com/goeastagent/MildInt