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Multi-PheWAS intersection approach to identify sex differences across comorbidities in 59 140 pediatric patients with autism spectrum disorder., , , , , , , , , and 1 other author(s). J. Am. Medical Informatics Assoc., 29 (2): 230-238 (2022)Finding commonalities in rare diseases through the undiagnosed diseases network., , , , , , , , , and 2 other author(s). J. Am. Medical Informatics Assoc., 28 (8): 1694-1702 (2021)The Drug Data to Knowledge Pipeline: Large-Scale Claims Data Classification for Pharmacologic Insight., , , , , and . CRI, AMIA, (2016)Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data., , , , , , , , and . PSB, page 295-306. (2020)Subtype Discovery for Autism Spectrum Disorder by Comorbidity Analysis., , and . AMIA, AMIA, (2017)Active learning for sampling in time-series experiments with application to gene expression analysis., , , , and . ICML, volume 119 of ACM International Conference Proceeding Series, page 832-839. ACM, (2005)There is Variability in the Accuracy of Diagnosis Codes Used to Identify COVID-19 Patients., , , , , , , , and . AMIA, AMIA, (2021)Making sense out of massive data by going beyond differential expression., , , and . Proc. Natl. Acad. Sci. USA, 109 (15): 5594-5599 (2012)Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data, , , , , , , , and . (2018)cite arxiv:1804.01486.Clinical Concept Embeddings Learned from Massive Sources of Medical Data., , , , , , and . CoRR, (2018)