Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions.

Introduction: Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD.

Methods: We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center.

Potential impact of blood cholesterol guidelines on statin treatment in the U.S. population using interrupted time series analysis.

Background: The 2013 ACC/AHA Guideline was a paradigm shift in lipid management and identified the four statin-benefit groups. Many have studied the guideline's potential impact, but few have investigated its potential long-term impact on MACE. Furthermore, most studies also ignored the confounding effect from the earlier release of generic atorvastatin in Dec 2011.

Decision rules for personalized statin treatment prescriptions over multi-objectives.

In our previous study, we demonstrated the feasibility of producing a proactive statin prescription strategy - a personalized statin treatment plan (PSTP) - using neural networks with big data. However, its non-transparency limited result interpretations and clinical usability. To improve the transparency of our previous approach with minimal compromise to the maximal statin treatment benefit-to-risk ratio, this study proposed a five-step pipeline approach called the decision rules for statin treatment (DRST).

Personalized statin treatment plan using counterfactual approach with multi-objective optimization over benefits and risks.

Background: Statins are a class of drugs that lower cholesterol levels in the blood by inhibiting an enzyme called 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase. High cholesterol levels can lead to plaque buildup in the arteries, which can cause Atherosclerotic Cardiovascular Disease(ASCVD). Statins can reduce the risk of ASCVD events by about 25-35% but they might be associated with symptoms such as muscle pain, liver damage, or diabetes.

Variant Characterization of a Representative Large Pedigree Suggests "Variant Risk Clusters" Convey Varying Predisposition of Risk to Lynch Syndrome.

Recently, worldwide incidences of young adult aggressive colorectal cancer (CRC) have rapidly increased. Of these incidences diagnosed as familial Lynch syndrome (LS) CRC, outcomes are extremely poor. In this study, we seek novel familial germline variants from a large pedigree Tunisian family with 12 LS-affected individuals to identify putative germline variants associated with varying risk of LS.

Somatic mutation variant analysis in rural, resectable non-small cell lung carcinoma patients.

Rural non-small cell lung cancer (NSCLC) patients do worse, largely related to lack of access to care. In this study, the mutational characteristics and potential for targeted therapy in rural, resectable NSCLC patients using whole exome sequencing (WES) were analyzed. WES was performed on tumor-adjacent normal pairs from rural patients undergoing resection for NSCLC.

Single Circulating-Tumor-Cell-Targeted Sequencing to Identify Somatic Variants in Liquid Biopsies in Non-Small-Cell Lung Cancer Patients.

Non-small-cell lung cancer (NSCLC) accounts for most cancer-related deaths worldwide. Liquid biopsy by a blood draw to detect circulating tumor cells (CTCs) is a tool for molecular profiling of cancer using single-cell and next-generation sequencing (NGS) technologies. The aim of the study was to identify somatic variants in single CTCs isolated from NSCLC patients by targeted NGS.

Evaluation of cfDNA as an early detection assay for dense tissue breast cancer.

A cell-free DNA (cfDNA) assay would be a promising approach to early cancer diagnosis, especially for patients with dense tissues. Consistent cfDNA signatures have been observed for many carcinogens. Recently, investigations of cfDNA as a reliable early detection bioassay have presented a powerful opportunity for detecting dense tissue screening complications early.

Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning.

Almost half of Americans 65 years of age and older take statins, which are highly effective in lowering low-density lipoprotein cholesterol, preventing atherosclerotic cardiovascular disease (ASCVD), and reducing all-cause mortality. Unfortunately, ∼50% of patients prescribed statins do not obtain these critical benefits because they discontinue use within one year of treatment initiation. Therefore, statin discontinuation has been identified as a major public health concern due to the increased morbidity, mortality, and healthcare costs associated with ASCVD.

Western influenced lifestyle and Kv2.1 association as predicted biomarkers for Tunisian colorectal cancer.

Background: Colorectal cancer (CRC) is the third most diagnosed malignancy worldwide. The global burden is expected to increase along with ongoing westernized behaviors and lifestyle. The etiology of CRC remains elusive and most likely combines environmental and genetic factors.

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

Background: Clinical trials testing pharmacogenomic-guided warfarin dosing for patients with atrial fibrillation have demonstrated conflicting results. Non-vitamin K antagonist oral anticoagulants are expensive and contraindicated for several conditions. A strategy optimizing anticoagulant selection remains an unmet clinical need.

Using simulation and optimization approach to improve outcome through warfarin precision treatment.

We apply a treatment simulation and optimization approach to develop decision support guidance for warfarin precision treatment plans. Simulation include the use of ∼1,500,000 clinical avatars (simulated patients) generated by an integrated data-driven and domain-knowledge based Bayesian Network Modeling approach. Subsequently, we simulate 30-day individual patient response to warfarin treatment of five clinical and genetic treatment plans followed by both individual and subpopulation based optimization.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.

Optimal decision support rules improve personalize warfarin treatment outcomes.

We tested optimization-based approaches to generate decision support rules used to improve personalized warfarin treatment based on clinical and genetic characteristics. Our approach simulated warfarin treatment outcomes using five existing treatment plans for clinical avatars (virtual patients). We used individual clinical avatar Time-in-Therapeutic-Range to represent the two-sided adverse risk to bleeding (over dosed - above therapeutic range) and thrombosis (under dosed - below therapeutic range) and as the objective function in the optimization to minimize overall risk.

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

Background: Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost.

Big Data Cohort Extraction to Facilitate Machine Learning to Improve Statin Treatment.

Health care Big Data studies hold substantial promise for improving clinical practice. Among analytic tools, machine learning (ML) is an important approach that has been widely used by many industries for data-driven decision support. In Big Data, thousands of variables and millions of patient records are commonly encountered, but most data elements cannot be directly used to support decision making.

Scalable and cost-effective NGS genotyping in the cloud.

Background: While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10's of dollars.

Results: We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow.

Histopathologic alterations associated with global gene expression due to chronic dietary TCDD exposure in juvenile zebrafish.

The goal of this project was to investigate the effects and possible developmental disease implication of chronic dietary TCDD exposure on global gene expression anchored to histopathologic analysis in juvenile zebrafish by functional genomic, histopathologic and analytic chemistry methods. Specifically, juvenile zebrafish were fed Biodiet starter with TCDD added at 0, 0.1, 1, 10 and 100 ppb, and fish were sampled following 0, 7, 14, 28 and 42 d after initiation of the exposure.

COSMOS: Python library for massively parallel workflows.

Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow.

Streaming support for data intensive cloud-based sequence analysis.

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of "resources-on-demand" and "pay-as-you-go", scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services.

Differential gene expression associated with dietary methylmercury (MeHg) exposure in rainbow trout (Oncorhynchus mykiss) and zebrafish (Danio rerio).

The objective of this study was to identify and evaluate conserved biomarkers that could be used in most species of teleost fish at most life-stages. We investigated the effects of sublethal methylmercury (MeHg) exposure on developing rainbow trout and zebrafish. Juvenile rainbow trout and young adult zebrafish were fed food with MeHg added at 0, 0.

Ultradense array CGH and discovery of micro-copy number alterations and gene fusions in the cancer genome.

The characterization of molecular alterations specific to cancer facilitates the discovery of predictive and prognostic biomarkers important to targeted therapeutics. Alterations critical to cancer therapeutics include copy number alterations (CNAs) such as gene amplifications and deletions as well as genomic rearrangements resulting in gene fusions. There are two genome-wide technologies used to detect CNAs: next generation sequencing (NGS) and dense microarray based comparative genomic hybridization, termed array CGH (aCGH).

A systems approach to designing effective clinical trials using simulations.

Background: Pharmacogenetics in warfarin clinical trials have failed to show a significant benefit in comparison with standard clinical therapy. This study demonstrates a computational framework to systematically evaluate preclinical trial design of target population, pharmacogenetic algorithms, and dosing protocols to optimize primary outcomes.

Methods And Results: We programmatically created an end-to-end framework that systematically evaluates warfarin clinical trial designs.