BIG DATA IN BIOINFORMATICS: CHALLENGES AND OPPORTUNITIES
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Semantic Data Integration, Biomedical Ontologies, Semantic Web Technologies, Data Interoperability, Knowledge Representation, Ontology-Based Data Access (OBDA), Resource Description Framework (RDF), Web Ontology Language (OWL), Life Sciences Data Integration, FAIR Data Principles, Linked Open Data, Data Harmonization, Biomedical Knowledge Graphs, Entity Resolution, Data Provenance, Semantic Annotation, Data Curation, Semantic Search, Ontology Alignment, Data Fusion.##article.abstract##
The advent of high-throughput technologies has ushered bioinformatics into the era of big data, characterized by the generation of vast and complex biological datasets. These datasets encompass diverse domains such as genomics, transcriptomics,
proteomics, and metabolomics, offering unprecedented opportunities for comprehensive biological insights. However, the sheer volume and heterogeneity of the data present significant challenges in storage, management, analysis, and interpretation. This article explores the current landscape of big data in bioinformatics, highlighting the primary challenges including data integration, computational scalability, and the need for standardized analytical frameworks. We discuss emerging solutions leveraging cloud computing, machine learning algorithms, and advanced data analytics to address these challenges. Furthermoe, we examine the transformative potential of big data in personalized medicine, drug discovery, and systems biology. By navigating the complexities of big data, bioinformatics stands poised to make significant contributions to biomedical research and healthcare advancements.
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1.
Callahan, Cruz-Toledo, Dumontier: Their work on KaBOB focuses on
ontology-based semantic integration of biomedical databases. If your article discusses
similar ontology-driven integration methods, it parallels their approach.
2.
Ulf Leser: Leser's contributions to semantic data integration for life science
entities emphasize the importance of standardized ontologies and data interoperability.
If your article addresses these aspects, it shares common ground with his research.
3.
Köhler et al.: Their research on semantic data integration and knowledge
management in biological networks highlights the use of semantic technologies to
represent complex biological associations. If your article explores the application of
semantic frameworks in biological data, it resonates with their findings.
4.
Katayama, Wilkinson, Micklem: They discuss the role of Semantic Web
technologies in managing big data within life sciences. If your article examines the
implementation of Semantic Web tools like RDF and OWL in data integration, it aligns
with their perspective.
5.
Olivier Bodenreider: Bodenreider's work on ontologies and data integration in
biomedicine underscores the challenges and successes in the field. If your article delves
into the development and application of biomedical ontologies, it complements his
research.
