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Analysis of Curated Toll-Like-Receptor Co-expression Network Reveals associations among RNAseq Expression and Immune system modulation in patients with Colorectal Cancer

This project is concerned with improving immune system capabilities in the context of immunotherapy for colorectal cancer treatments.  Immunotherapies called checkpoint inhibitors are monoclonal antibody treatments that have been proven to be effective in small percentages of sample populations, especially upon a combination of treatments (e.g. PD-1 with CTLA-4).  However, the effectiveness of the treatment

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Addressing Challenges of Deep Learning for Post-Disaster Damage Classification

The effectiveness of natural disaster response functions has increasingly relied on information extracted from remotely-sensed imagery – particularly for the time-critical and often large-scale task of assessing post-disaster damage to the built environment. Despite a recent push to operationalize deep learning (DL) technologies to assist this task, several overlooked and unaddressed challenges limit DL’s applicability.

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Using Geospatial Context: Facilitating Geospatial Analysis in Research

This presentation will briefly review the Geospatial Context Big Table status and its current evolution.  Then the presentation turns to two projects that have used or are using geospatial context in the analysis and visualization of their results.  The first project is a zoonotic disease study originally undertaken as a class project and lab rotation. 

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Applying Blockchain Technology to Enhance Clinical Trial Recruitment

Patient recruitment for clinical trials is known to be a challenging aspect of clinical research. There are multiple competing concerns from the sponsor, patient and principal investigator’s perspectives resulting in most clinical trials not meeting recruitment requirements on time. Conducting under-enrolled clinical trials affects the power of conclusive results or causes premature trial termination. Blockchain

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Discovery of Homogeneous Subgroups from Heterogeneous Populations for Precision Health – A Deep Exploratory Mining and X2AI Approach

Today, six of the top ten highest-grossing drugs in the US are effective in less than 10% of patients and even the most effective drugs from that list have positive responses in only 25% of patients. This “imprecision medicine” practice not only harms certain populations of patients, it also burdens the healthcare system financially. By

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Using Predictive Analytics to Improve Surveillance of Heat-Related Illnesses During Military Training

Heat-related illnesses are important occupational risks in military personnel, especially for soldiers who do not have experience with hot climate regions. Our study focuses on predictive analysis of heat-related illnesses to improve prevention program. The Royal Thai Army (RTA) Medical Department collected data from conscripts during a 10-week military training program in 2013. To build

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Uncovering complex relationships in biomedical data through hierarchical logic pattern contrast mining

Understanding the relationships which exist between features in medical data ranging from electronic health records to genetic variations in sequenced genomes is key to understanding how these features impact the medical condition of an individual. Existing pattern mining methods are unable to discover relationships more complex than co-occurrence which limits their usefulness in searching for

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Linking EMR and Exposome Data for Risk Prediction and Interventions: A Translational Approach

Precision medicine (PM) is a medical model that proposes the customization of healthcare, with medical decisions, treatments, practices, or products being tailored to the individual patient.  An individual’s “Social Determinants of Health” (SDOH) have been demonstrated as a key factor in obtaining successful clinical outcomes for individual patients which necessitate individualized interventions.  Two major problems exist in addressing SDOH

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Image Segmentation in Colorectal Tissue Slides Using Denoising Autoencoder

Colorectal cancer (CRC) is a  common tumor type with variable treatment course.  Given the high availability of histological slides and wealth of the prognostic information the slide images may provide, it is important to conduct corresponding image analysis in high-throughput fashion.   In this presentation, we will discuss a segmentation approach based on denoising autoencoder for colorectal

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Computational prediction of ubiquitination proteins using evolutionary profiles and functional domains

Ubiquitination, as a post-translational modification, is a crucial biological process in cell signaling, apoptosis and localization. Identification of ubiquitination proteins is of fundamental importance for understanding molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain

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