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DeepVariant – TrioTrain: Developing a transfer learning protocol using non-human genomes

Genomic data are widely available for investigating phenotypes that impact both human and animal health. Although the investigation of human health often begins with model organisms, genomics technologies and software are often initially developed with only the human genome in mind, severely limiting their comparative applicability. Translational research requires robust systems-focused, or “One Health,” solutions

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A combined AI approach to biomedical data analysis: Knowledge representation reasoning, machine learning and explainable AI

In this talk, I will explore if and how two traditionally distinct fields of AI, that is, ontology engineering and machine learning can be combined to improve performance outcomes. Using real world examples from epilepsy neurological disorder, the talk will demonstrate the use of biomedical ontologies in machine learning workflows to address the critical challenge of feature engineering in

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Exploratory analysis of the use of Telemedicine in Primary care

This research is primarily focused on use of Telemedicine in Primary care and how that usage changed over time especially COVID 19. In this research, we did a scoping review to see how Primary care adapted Telemedicine during COVID-19 and what are some of the successes or challenges with the adaptation. In this research, we

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Identifying Gene-Gene Interactions Protective Against Autism Using Contrast Mining

Many genetic variants have been linked with the development of ASD. ASD is also known to be more prevalent in males than in females. The underlying mechanism for this difference is unclear. The polygenic nature of the genetic component of ASD makes studying potential mechanisms difficult if the significance of variants is assessed independently, as

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Early Warning of Health Changes for Older Adults: Implementing a Gaussian Mixture Components Clustering Algorithm to Detect Outliers in Daily Multi-feature Sensor Data Streams

In this case study, we evaluate the implementation of Sequential Possibilistic Gaussian Mixture Models (SPGMM) for accurately modeling changes in feature streams antecedent to known health events, thereby providing predictive relevance for clinical use, including identifying the preprocessing requirements for streams prior to algorithm input. SPGMM is a change detection algorithm developed for use in

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Supporting Population Health Outcomes Studies Using a Framework of Social Determinants Linked EHR Data

Population health outcomes research based on social determinants of health (SDoH) needs to link electronic health record (EHR) data with social determinants using Identifiable information (patients’ addresses). The connectivity expects additional computational load, privacy risk, and storage for each research. A Data Lake that facilitates research data can provide a framework for SDoH-connected EHR data

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Alzheimer’s disease mitigation: AI, neuroimaging and gut-brain axis

Alzheimer’s disease (AD) is the most common form of dementia and currently there are no effective therapeutics to reverse the course once the clinical symptoms have developed. Early identification of risk factors for AD and effective interventions thereof would be critical to mitigate AD pathological development and prevent the onset of clinical symptoms. In the

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Overhead imagery training data quality control: Methods for deep feature label anomaly detection

Spatial analysis of large remotely-sensed imagery (RSI) training datasets for within-class variation and between-class separability is key to uncovering issues of data diversity and potential bias, not just when vetting datasets for usage, but also during the actual dataset creation stage. Project managers of complex imagery annotation campaigns have a largely unaddressed need for tools that continuously

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Biological pathways as graphs: comparison of select similarity methods

We extracted biomedical pathways from 47 publications related to non-small cell lung cancer (NSCLC) and mergedthem into a Neo4j graph database. With this graph serving as ground truth for comparing to other pathways that were extracted from other publications, we investigated several methods of calculating graph similarity. Unlike ontologies and engineered data sets that have uniform representations of data objects, graphs extracted from unstructured texts haveto be compared as text-described entities first, and by using common graph similarity methods second. In this work, we discuss ways of comparing biological graphs composed of text-described

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Impact of diabetes status and other factors on risk for thrombotic and thromboembolic events: A multicenter, retrospective analysis using the Cerner Real-World DataTM de-identified COVID-19 cohort 

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a proinflammatory condition that can impact the cardiovascular and cerebrovascular systems, thereby increasing risk for thrombotic and thromboembolic events (TTE). However, little is known about the impact of diabetes status on risk for TTEs during SARS-CoV-2 infection. In this US-based, multicenter

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