Sep. 5, 2017

Analysis of Influence of Additional Diagnostic Clues During Pathology Diagnosis

Traditional pathology diagnostic process routinely relies on disease-specific diagnostic clues. We propose an informatics pipeline to identify and quantify additional diagnostic clues that, in addition to traditional disease-specific clues, can improve diagnostic outcomes and decrease the chance of diagnostic pitfalls. We used our PathEdEx whole-slide imaging platform to record user activities related to diagnosing a cancerous tissue slide along with the biological features that were noted in the tissue by the examining pathologist as relevant to the diagnosis. To identify and quantify additional diagnostic clues that can improve diagnosis, we extended association rule mining techniques to measure information gain of the additional…

Aug. 11, 2017

A Blockchain Platform for Healthcare

Although Blockchain is much more investigated within the financial sector, it is gradually edging into other industries such as healthcare. Blockchain provides a trust mechanism in a cyber-system. Various last mile issues left to be resolved for establishing an end to end trust cyber physical system. This talk will discuss various aspects of blockchain last mile issues applied in the medical area. Especially, for data integrity, trust and secure data sharing technology specially fit for precision medicine supporting patient centric healthcare model. This is an international collaboration project among Asia University, Taiwan, China Medical University Hospital, Taiwan, University of Missouri,…

May 1, 2017

White Mothers’ Willingness to Share Personal Health Data: Survey Results and Future Research Implication

The objective of this research is to understand white mothers’ willingness to share their personal health data (PHD). Studies have shown that participants are willing to share their data when there is a benefit to do so, and when their data are kept private and secure. We recruited a representative sample of white mothers with children without disabilities from an opt-in panel to assess their willingness to share their PHD. We surveyed these mothers on their attitudes and beliefs and trust in data sharing, data sharing thru devices, internet use and interest in future research, and questions related to caregivers…

April 21, 2017

Characterizing Physician EMR Workflow With Clustering and Hilbert Space Filling Curve Visualizations

Understanding sequences of events is paramount to developing a picture of how clinicians interact with other clinicians, patients, and Electronic Medical Records (EMR).  Common methods of assessing clinical workflow include qualitative methods such as video recording, direct observations, or directed user experience testing in a controlled environment. Studies such as these are designed to help understand traditional workflow concepts such as time and motion efficiency, interpersonal communication, and information needs.  Applications for EMR workflow include measuring productivity to help target training, and dynamic EMR experiences based on predicted actions.  Computational, or quantitative, methods can also be applied, such as sequential…

April 7, 2017

Computationally-Determined FliC Linked Recognition Epitope Micelles for Burkholderia Vaccination

Human disease caused by Burkholderia spp. is a serious problem in many parts of the world including infection of immunocompromised patients and those with cystic fibrosis.  Traditional antimicrobial therapy is protracted and problematic. The goal of this cooperated structural vaccinology project is to identify novel immune dominant and cryptic linked B and T cell epitopes for the development of efficacious vaccines against Burkholderia mallei and B. pseudomallei flagellar protein FliC. For our in silico part works, we will screen the greater than 30 known genomic/proteomic data sets of Burkholderia spp. including both pathogenic and non-pathogenic strains.  Identified sequences of interest will be scored for inclusion of both a putative T…

March 29, 2017

Dynamically Predict Risk of 30-day Hospital Readmission for Diabetic Patients Using a Data Stream Mining Approach

Early hospital readmissions can negatively impact patients’ quality of life and hospitals’ income. Intensive efforts have been made to develop 30-day hospital readmission risk prediction models. Most of the reported statistical and machine learning models were built with static datasets. The reality, however, is that data arrives sequentially and the trend may evolve over time. As a result, many static models failed in practice. To make the models up-to-date, the traditional approach is re-training them from scratch periodically, which can be expensive. Most importantly, it is difficult to determine and discard outdated knowledge from the models using this approach. In…

March 20, 2017

Informatics Approaches to Uncover the Molecular Mechanisms of Endometriosis in Clinical Patients

Endometriosis, a complex and common gynecological disorder affecting 5–10% of reproductive-age women, is characterized by the growth of endometrial tissue outside of the uterine cavity. Accumulating evidence indicates that various epigenetic aberrations are associated with endometriosis. In our study, we have methylation data and clinical information on 80 patients (36 controls and 44 cases) from a clinical study. Our objectives are to identify the genomic regions associated with endometriosis and identify the specific genes associated with endometriosis after adjusting for potential confounding variables. We have developed a bioinformatics methylation data analysis pipeline in-house using several open source tools including FastQC,…

March 13, 2017

A Deep Natural Network Method for Predicting Mitochondrially Localized Proteins in Plants

Targeting and translocation of proteins to the appropriate subcellular compartments is crucial for cell organization and function. Some newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization for any given protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools is unsatisfactory. We present a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected…

March 6, 2017

Geonmic Selection using Deep Learning method

Genomic selection is an approach to enhance the quantitative traits in plant and animal breeding program at early stage using whole genome molecular markers, especially for long life-cycle species. It’s based on the assumption that all quantitative trait loci (QTL) tend to be in linkage disequilibrium with at least on marker. Statistical methods, such as ridge regression, best linear unbiased prediction (RR-BLUP)[1], Bayes A[2], Bayesian LASSO[3] are widely used for genomic selection problem works SNP matrix.  Other machine learning methods (random forrest, support vector machine and neural network)[4] are also been applied for this study. In this work, we are…