A5054086551- Biomedical Text Mining and Ontologies
- Bioinformatics and Genomic Networks
- Semantic Web and Ontologies
- Genomics and Phylogenetic Studies
- Frailty in Older Adults
- Topic Modeling
- Gene expression and cancer classification
- Delphi Technique in Research
- Conferences and Exhibitions Management
- Data Mining Algorithms and Applications
- Advanced Text Analysis Techniques
- Genetics, Bioinformatics, and Biomedical Research
- Health Systems, Economic Evaluations, Quality of Life
- Machine Learning in Bioinformatics
- Expert finding and Q&A systems
- Natural Language Processing Techniques
- Species Distribution and Climate Change
- Time Series Analysis and Forecasting
- Chronic Disease Management Strategies
- Sentiment Analysis and Opinion Mining
- Insect and Arachnid Ecology and Behavior
- Hip and Femur Fractures
- Heart Failure Treatment and Management
- Law, AI, and Intellectual Property
- Insect and Pesticide Research
University of Nebraska at Omaha
2025
University of North Carolina at Greensboro
2017-2023
UnitedHealth Group (United States)
2023
University of North Carolina at Chapel Hill
2015-2016
National Evolutionary Synthesis Center
2015
Mississippi State University
2009-2013
Healthcare costs due to unplanned readmissions are high and negatively affect health wellness of patients. Hospital readmission is an undesirable outcome for elderly Here, we present risk prediction using five machine learning approaches predicting 30-day patients (age ≥ 50 years). We use a comprehensive curated set variables that include frailty, comorbidities, high-risk medications, demographics, hospital, insurance utilization build these models. conduct large-scale study with electronic...
AgBase (http://www.agbase.msstate.edu/)provides resources to facilitate modeling of functional genomics data and structural annotation agriculturally important animal, plant, microbe parasite genomes.The website is redesigned improve accessibility ease use, including improved search capabilities.Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice soybean.We currently provide 590 240 Gene Ontology (GO) annotations 105 454 gene products in 64 different species,...
Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application numerous areas such as discovery of disease genes drug targets, phylogenetics pharmacogenomics. Phenotypes, defined observable characteristics organisms, can be seen one bridges that lead a translation experimental findings into applications thereby support 'bench bedside' efforts. However, build this translational bridge, common universal understanding phenotypes is required goes...
Ontologies are critical for organizing and interpreting complex domain-specific knowledge, with applications in data integration, functional prediction, knowledge discovery. As the manual curation of ontology annotations becomes increasingly infeasible due to exponential growth biomedical genomic data, natural language processing (NLP)-based systems have emerged as scalable alternatives. Evaluating these requires robust semantic similarity metrics that account hierarchical partially correct...
The Gene Ontology (GO), a set of three sub-ontologies, is one the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in ontologies an important source implicit relationships between sub-ontologies. Data mining techniques such as association rule that are tailored to mine abstraction required effective knowledge discovery data. We present approach, Multi-ontology All Levels...
There is a growing interest in using social media content for Natural Language Processing applications. However, it not easy to computationally identify the most relevant set of tweets related any specific event. Challenging semantics coupled with different ways natural language make difficult retrieving data from outlet. This paper seeks demonstrate way present changing Twitter within context crisis event, specifically during Hurricane Irma. These methods can be used corpus text analysis...
The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. wealth GO annotation data provides a valuable source implicit knowledge relationships among these aspects. We describe new method association rule mining to discover co-occurrence across sub-ontologies at multiple levels abstraction. Prior work on in concentrated single level abstraction and/or between terms from same sub-ontology. have developed...
Natural language descriptions of organismal phenotypes, a principal object study in biology, are abundant the biological literature. Expressing these phenotypes as logical statements using ontologies would enable large-scale analysis on phenotypic information from diverse systems. However, considerable human effort is required to make phenotype amenable machine reasoning. processing tools have been developed facilitate this task, and training evaluation depend availability high quality,...
The widespread availability of microarray technology has driven functional genomics to the forefront as scientists seek draw meaningful biological conclusions from their results. Gene annotation enrichment analysis is a technique that gained attention and for which many tools have been developed. Unfortunately, most these limited support agricultural species. Here, we evaluate compare four publicly available computational (Onto-Express, EasyGO, GOstat, DAVID) gene expression datasets in We...
Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating molecular mechanisms govern biology under investigation. A number of publicly available 'discovery-based' computational tools use computationally amenable Gene Ontology (GO) hypothesis generation. However, there few support...
Representing scientific knowledge using ontologies enables data integration, consistent machine-readable representation, and allows for large-scale computational analyses. Text mining approaches that can automatically process annotate literature with ontology concepts are necessary to keep up the rapid pace of publishing. Here, we present deep learning models (Gated Recurrent Units (GRU) Long Short Term Memory (LSTM)) combined different input encoding formats automated Named Entity...
Abstract The gene ontology (GO) is a widely used resource for describing molecular functions, biological processes, and cellular components of products. Since its inception in 2006, the GO has been to describe millions products resulting massive data store over 6 million annotations. staggering amount that resulted from annotating with terms led way opened new avenues wide variety large‐scale computational analyses. Specifically, mining techniques such as association rule mining, clustering...
The purpose of the current study was to investigate predictive properties five definitions a frailty risk score (FRS) and three comorbidity indices using data from electronic health records (EHRs) hospitalized adults aged ≥50 years for 3-day, 7-day, 30-day readmission, identify an optimal model FRS combination. Retrospective analysis EHR dataset performed, multivariable logistic regression area under curve (AUC) were used examine readmission comorbidity. sample ( N = 55,778) mostly female...
I. Abstract Text mining approaches for automated ontology-based curation of biological and biomedical literature have largely focused on syntactic lexical analysis along with machine learning. Recent advances in deep learning shown increased accuracy textual data annotation. However, the application is a relatively new area prior work has limited set models. Here, we introduce model/architecture based combining multiple Gated Recurrent Units (GRU) character+word input. We use from five...
Manual curation of scientific literature for ontology-based knowledge representation has proven infeasible and unscalable to the large growing volume literature. Automated annotation solutions that leverage text mining Natural Language Processing (NLP) have been developed ameliorate problem curation. These NLP approaches use parsing, syntactical, lexical analysis recognize annotate pieces with ontology concepts. Here, we conduct a comparison four state art tools at task recognizing Gene...
1 Abstract Semantic similarity has been used for comparing genes, proteins, phenotypes, diseases, etc. various biological applications. The rise of ontology-based data representation in biology also led to the development several semantic metrics that use different statistics estimate similarity. Although become a crucial computational tool applications, there not formal evaluation statistical sensitivity these and their ability recognize between distantly related objects. Here, we present...
The study of how the observable features organisms, i.e., their phenotypes, result from complex interplay between genetics, development, and environment, is central to much research in biology. varied language used description however, impedes large scale interdisciplinary analysis phenotypes by computational methods. Phenoscape project (www.phenoscape.org) has developed semantic annotation tools a gene–phenotype knowledgebase, KB, that uses machine reasoning connect evolutionary comparative...
Manual curation of scientific literature for ontology-based knowledge representation has proven infeasible and unscalable to the large growing volume literature. Automated annotation solutions that leverage text mining Natural Language Processing (NLP) have been developed ameliorate problem curation. These NLP approaches use parsing, syntactical, lexical analysis recognize annotate pieces with ontology concepts. Here, we conduct a comparison four state art tools at task recognizing Gene...
Conferences with contributed talks grouped into multiple concurrent sessions pose an interesting scheduling problem. From attendee’s perspective, choosing which to visit when there are many is challenging since individual may be interested in topics that discussed different simultaneously. The frequency of topically similar is, fact, a common cause for complaint post-conference surveys. Here, we introduce practical solution the conference problem by heuristic optimization objective function...
There is growing use of ontologies for the measurement cross-species phenotype similarity. Such similarity measurements contribute to diverse applications, such as identifying genetic models human diseases, transferring knowledge among model organisms, and studying basis evolutionary innovations. Two organismal features, whether genes, anatomical parts, or any other inherited feature, are considered be homologous when they evolutionarily derived from a single feature in common ancestor. A...
Abstract In phenotype annotations curated from the biological and medical literature, considerable human effort must be invested to select ontological classes that capture expressivity of original natural language descriptions, finer annotation granularity can also entail higher computational costs for particular reasoning tasks. Do coarse suffice certain applications? Here, we measure how affects statistical behavior semantic similarity metrics. We use a randomized dataset profiles drawn...