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Information Extraction Framework for Facilitating the Assessment of the Quality of Radiology Interpretations

Assessing the quality of imaging interpretations requires that the results of radiological interpretations be compared with those of subsequent surgical-pathology results, when available. The manual process is inherently slow, tedious and expensive, and unless systematic errors occur in the interpretations, discrepancies are unlikely to be detected. Classical computational methods using Natural Language Processing entail using

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Abnormal liver function and adverse health outcomes in COVID-19: A multicenter, retrospective analysis of 14,872 patients from 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), has been associated with liver impairment and abnormalities in liver function tests.  However, associations between hepatic impairment and patient health outcomes have not been well-studied in large cohorts.  In this US-based, multicenter retrospective cohort study, we analyze the impact of abnormalities in liver function

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Purdue’s New Plant Sensor Technologies for Improved Phenotyping Quality

Plant phenotyping technologies have been developing rapidly over the last 2 decades. Plant sensors are becoming more precise, faster, easier to use and with lower cost. However, there are still several bottleneck issues in plant sensing, including the changing environmental conditions, the great variances on the plant, and the complicated GxTxEinteractions. These issues keep phenotyping difficult

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Thyroid Cancer Informatics

Survival prediction is important both to clinicians and patients; ensuring the best course of treatment is selected to manage the thyroid cancer. In 2018, there was an estimated half a million new thyroid cancer diagnoses and 41,071 deaths. Unlike other tumors whose mortality has decreased over the last two decades, thyroid cancer mortality rates have increased. Existing risk

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A Case-Control based Genomic Analysis of Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) is a respiratory illness that affects millions of people all over the world. It is a major cause of chronic morbidity and mortality and a serious global public health problem. COPD is the fourth leading cause of death worldwide. Although the environmental causes of COPD which predominantly include cigarette smoking

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Methods for Measuring Geodiversity in Large Overhead Imagery Datasets

This research introduces some of the first geo-computational methods to address a key gap in the artificial intelligence (AI) and big data literature as it relates to the geosciences and remote sensing: the lack of understanding of the global feature representativeness of labels in large remotely-sensed imagery (RSI) datasets. Issues of data fairness, heterogeneity and

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Teaching Professor Faculty Positions in Data Science and Analytics

 Description: The University of Missouri (MU) Institute for Data Science and Informatics (IDSI) is accepting applications for multiple positions of Teaching Professor (Assistant and Associate levels) of Data Science and Analytics. In today’s information-centric world, data are becoming increasingly important for the success of businesses in every industry. Data science at MU is an interdisciplinary

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The Genescape Allele Catalog Development for Precise Identification of Causative SNPs

Next-generation sequencing (NGS) has become more popular in the modern-day. Large amounts of next-generation resequencing data have been generated and are available online for various organisms including soybeans. However, current genome-wide association study (GWAS) prediction tools simply identify the most significant SNP based on Manhattan plots and still have some limitations in pinpointing the exact

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Building a Population-based Childhood Cancer Data Ecosystem: Challenges and Opportunities for Informatics and Data Science

Childhood cancer is a relatively rare disease diagnosed in over 16,000 U.S. children and adolescents (ages 0 – 19) each year.  While 84% of children with cancer survive 5 years or more, cancer remains the second leading cause of death in children after accidents. Molecular variations make all childhood cancers extraordinarily rare and difficult to study.  The

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Evaluating the effectiveness of transfer-learning with DeepVariant

Genomic data has become ubiquitous for bioinformaticians; however, successfully inferring biological meaning depends upon the sensitive prediction of differences between genomes. The most popular method to infer short sequence variants is the Genome Analysis Toolkit (GATK). While GATK provides rigorous guidelines, the methods require knowledge-intensive refinement as software and sequencing technologies advance. A recent advancement

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