A5046076186- Semantic Web and Ontologies
- Scientific Computing and Data Management
- Data Quality and Management
- Biomedical Text Mining and Ontologies
- Service-Oriented Architecture and Web Services
- Advanced Database Systems and Queries
- Healthcare Systems and Public Health
- Species Distribution and Climate Change
- Explainable Artificial Intelligence (XAI)
- Environmental DNA in Biodiversity Studies
- Natural Language Processing Techniques
- Artificial Intelligence in Healthcare and Education
- Identification and Quantification in Food
- Microbial Community Ecology and Physiology
- Logic, Reasoning, and Knowledge
- Complex Systems and Decision Making
- Data-Driven Disease Surveillance
- IoT and Edge/Fog Computing
- Musculoskeletal pain and rehabilitation
- Health, Environment, Cognitive Aging
- Data Stream Mining Techniques
- Data Mining Algorithms and Applications
- Mosquito-borne diseases and control
- Cell Image Analysis Techniques
- Malaria Research and Control
University of Virginia
2020-2024
University of New Brunswick
2011-2021
Dalhousie University
2021
Biomedical research and clinical practice are in the midst of a transition toward significantly increased use artificial intelligence (AI) machine learning (ML) methods. These advances promise to enable qualitatively deeper insight into complex challenges formerly beyond reach analytic methods human intuition while placing demands on ethical explainable (XAI), given opaque nature many deep The U.S. National Institutes Health (NIH) has initiated significant development program, Bridge2AI,...
Threatened freshwater ecosystems urgently require improved tools for effective management. Food web analysis is currently under-utilised, yet can be used to generate metrics support biomonitoring assessments by measuring the stability and robustness of ecosystems. Using a previously developed pipeline, we combined taxonomic outputs from DNA metabarcoding with text-mining routine extract trait information directly literature. This pipeline allowed us heuristic food webs sites within lower...
<ns3:p>Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used the life sciences, though their composition has remained a cumbersome manual process due to lack standards for annotation, assembly, and implementation. Recent technological advances returned long-standing vision workflow into focus.</ns3:p><ns3:p> This article summarizes recent Lorentz Center workshop dedicated sciences. We survey...
According to the World Health Organization, malaria surveillance is weakest in countries and regions with highest burden. A core obstacle that data required perform are fragmented multiple silos distributed across geographic regions. Furthermore, consistent integrated sources few, a low degree of interoperability exists between them. As result, it difficult identify disease trends plan for effective interventions.We propose Semantics, Interoperability, Evolution Malaria Analytics (SIEMA)...
Semantic Querying (SQ) is emerging as an attractive approach for retrieval of data from relational and other conceptually similar databases, targeting users with limited or no technical expertise. Using SQ queries can be formulated using terminologies a specific domain, which are then either translated in real time into the equivalent SQL queries, executed against materialised semantic database obtained by transforming source data. This suitable non-technical who familiar describing domain...
Malaria is an infectious disease affecting people across tropical countries. In order to devise efficient interventions, surveillance experts need be able answer increasingly complex queries integrating information coming from repositories distributed all over the globe. This, in turn, requires extraordinary coding abilities that cannot expected non-technical experts. this paper, we present a deployment of Semantic Automated Discovery and Integration (SADI) Web services for federation...
Abstract Motivation Artificial intelligence (AI) applications require explainability (XAI) for FAIR, ethical deployment, whether in the clinic or laboratory. Richly descriptive XAI metadata representing how pre-model data were obtained, characterized, transformed, and distributed, should be available along with prior to training application of AI models. Results The FAIRSCAPE framework generates, packages, integrates critical metadata, including deep provenance graphs dictionaries feature...
Abstract Global health surveillance and pandemic intelligence rely on the systematic collection integration of data from diverse distributed heterogeneous sources at various levels granularity. These include multiple disciplines represented in different formats, languages, structures posing significant challenges This article provides an overview driven surveillance. Using Malaria as a use case we highlight contribution made by emerging semantic federation technologies that offer enhanced...
Informational needs of agricultural consultants are increasingly complex. Advising farmers on the appropriate measures for optimizing cropping yields demands access to custom data archives and analytics tools. In line with increasing number archives, expertise required goes beyond capabilities these non-technical agri-specialists. These end users have diverse ad-hoc query require tools that provide simple distributed silos easy ways integrate relevant information. this article, authors...
<h3>Introduction</h3> Ongoing studies into the use of algorithms for automated coding job titles to Canadian National Occupation Classification have performance accuracy which are at least equivalent manual accuracy. Moreover provides significant time savings. These identified that both natural language processing and machine learning effective auto coding. Whereas NLP based approaches rely on bespoke rules, existing data sets, models can proliferate bias from training if not corrected....
<sec> <title>BACKGROUND</title> According to the World Health Organization, malaria surveillance is weakest in countries and regions with highest burden. A core obstacle that data required perform are fragmented multiple silos distributed across geographic regions. Furthermore, consistent integrated sources few, a low degree of interoperability exists between them. As result, it difficult identify disease trends plan for effective interventions. </sec> <title>OBJECTIVE</title> We propose...
This paper reports on the early-stage development of an analytics framework to support semantic integration dynamic surveillance data across multiple scales inform decision making for malaria eradication. We propose using Semantic Web Things (SWoT), a combination Internet (IoT) and web technologies, evolution sources improve interoperability between different datasets generated through relevant IoT assets (e.g. computers, sensors, persons, other smart objects devices).
<h3>Introduction</h3> Occupational data in prospective cohort studies is often underutilized due to the human and financial resources required code open-ended text, such as job titles. Recognizing value of occupational health research, well potential errors associated with manual coding, an Automated Coding Algorithm (ACA)-NOC algorithm was developed utilizing a Natural Language Processing approach. <h3>Objectives</h3> We tested ACA-NOC on two regional cohorts pan-Canadian study, which...