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Medical errors and patient safety have been receiving significant attention since the landmark publication by Institute of Medicine in 2000. However, characteristics of physicians implicated in frequent medical errors were not studied systematically. We used National Practitioner Data Bank (NPDB) containing malpractice claims since 1990 to identify characteristics predictive of physicians with frequent...
Adverse Events (AEs) are a significant concern in healthcare, since it is among the leading causes of morbidity and mortality[12]. According to the Food and Drug Administration (FDA), between 2006 and 2014, there was a 232% increase in AE cases reported to have caused mortality[13]. In fact, the volume of all AE cases reported to the FDA has increased by almost five fold since 1997[13]. Pharmaceutical...
In biomedical research, events revealing complex relations between entities play an important role. Event trigger identification is a crucial and prerequisite step in the pipeline process of biomedical event extraction. There exist two main problems in the previous work: (1) Traditional feature-based methods often rely on human ingenuity, which is a time-consuming process. Though most representation-based...
Eldercare monitoring using non-wearable sensors is a candidate solution for improving care and reducing costs. Abnormal sensor patterns produced by certain resident behaviors could be linked to early signs of illness. We propose an unsupervised method for detecting abnormal behavior patterns based on a new context preserving representation of daily activities. A preliminary analysis of the method...
In recent years, there has been explosive growth in the amount of biomedical publications. In this paper, we propose a semantic framework that aims to automatically generate an ontology by extracting assertions and topics from multiple free-text scientific publications in PubMed. The pipeline approach for knowledge discovery and ontology generation in the proposed framework has been implemented on...
A fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by...
High-sensitivity C-reactive protein (hs-CRP) performs important roles on the onset of metabolic syndrome and cardiovascular diseases (CVD), but little is known about association between hs-CRP and obesity-related metabolic abnormalities in young people without classical CVD risk factors. It thus motivated us to investigate association among hs-CRP, body fat mass (FM) distribution, and other cardiometabolic...
In molecular biology, phenotypes are often described using complex semantics and diverse biomedical expressions, thereby facilitating the development of named entity recognition (NER). Here, we propose a novel approach of recognizing plant phenotypes by cascading word embedding to sentence embedding with a class label enhancement. We utilized a word embedding method to find high-frequency phenotypes...
The clinical decision support system can effectively solve the limitations of doctors' knowledge, reduce misdiagnosis and help enhance health. The traditional genetic data storage and analysis technology based on the stand-alone environment have limited scalability, which has been difficult to meet the computational requirements of rapid genetic data growth. In this paper, we propose a distributed...
Integration of multiple datasets grants in-silico investigations with higher statistical and reasoning power to elucidate secondary discoveries hidden to the initial data producers. Here we introduce a novel method for the network analysis of messenger RNA regulation. Post-translational regulation of gene activity by microRNA molecules is investigated, combining expression data and sequence binding...
SNOMED CT has continued to expand its adoption as a comprehensive clinical terminology. In 2013, the US government mandated SNOMED CT as a requirement for Stage 2 meaningful use criteria for Electronic Health Records. Studies have, however, identified inconsistencies in the content of SNOMED CT that may lessen its effectiveness when used for encoding patient data. Auditing thus becomes an integral...
Sequence overlap graphs, constructed based on suffix-prefix relationships between pairs of sequences, are an important data structure in computational biology. High throughput sequencers can read several million to a few billion DNA fragments in a single experiment, making the construction of overlap graphs for such datasets compute-intensive. In this paper, we present a Locality-Sensitive Hashing...
Protein Docking is an important task in computational chemistry and computational biology and yet is very computationally expensive. This paper presents a study on implementing molecular docking program in the Hadoop-based system. The molecular docking platform based on Hadoop provides the preprocessing of ligand datasets and the analysis function of the docking results, it also implements the parallel...
Genome-wide association studies (GWAS) are a type of genetic methods that have recently received intensive attention. In this paper, we study the construction of the Bayesian network from the GWAS catalog for modeling SNP and quantitative trait associations. Existing methods in the literature can only deal with categorical traits. We address this limitation by leveraging the Conditional Linear Gaussian...
Understanding epigenetic changes across various conditions is a fundamental problem to epigenome annotation. With more high-throughput epigenomic data available, computational methods have been developed to quantify various types of epigenetic modification signals, to compare epigenetic marks between different conditions and to understand the functional consequences of epigenetic changes. However,...
This work examines the validity of facial phenotypes as Autism Spectrum Disorders (ASD) biomarkers in boys with essential autism. A family-based association analysis framework is presented that uses previously identified facially-delineated (FD) clusters to examine relationship between FD clusters and known ASD genes. The hypothesis is that there are certain genetic variants, single nucleotide polymorphisms...
Antimicrobial peptides might become crucial in fighting antibiotic resistant bacteria and other infections. Next Generation Sequencing technologies are generating a large amount of data where peptides with antimicrobial activity could be found. Therefore, algorithms that can efficiently determine whether or not a short sequence of amino acids is antimicrobial are needed. In this context, Quantitative...
Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. We found that, in a GC study, the GC contents and read count ratios on SCNA segments present a Log linear biased pattern. However, currently no subclonal inferring tools...
Genome-wide association study (GWAS), as one primary approach for genetic studies, has been successfully applied to a variety of complex diseases, leading to the discovery of substantial disease-associated loci. These discovered associations provide unprecedented opportunities for deepening our understanding of complex diseases, such as disease-associated risk variants, genes, and pathways. However,...
Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote...
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