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Similar to unit 4 (2).pptx medical informatics lecture notes
unit 4 (2).pptx medical informatics lecture notes
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- 2. • Bioinformatics isdefined as, “the use of computer to store, retrieve, analyse or predict the composition or structure of bio-molecules” . • Bioinformatics is the application of computational techniques and information technology to the organisation and management of biological data. • Classical bioinformatics deals primarily with sequence analysis.
- 3. Aims of bioinformatics •Development of database containing all biological information. • Development of better tools for data designing, annotation and mining. • Design and development of drugs by using simulation software. • Design and development of software tools for protein structure prediction function, annotation and docking analysis. • Creation and development of software to improve tools for analysing sequences for their function and similarity with other sequences
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- 6. • Bio informationtechnologies are a subset of biotechnology that focuses on the application of computer technology and information science to manage, analyze, and interpret biological data. • This field combines computer science, mathematics, and biology to develop algorithms, statistical models, and software tools for understanding biological systems.
- 7. Key Bioinformation Technologies: •Genomics: Study of genome structure, function, and evolution. • Proteomics: Study of protein structure, function, and interactions. • Bioinformatics: Development of algorithms and statistical models for biological data analysis. • Computational Biology: Application of computational methods to model biological systems. • Systems Biology: Study of complex biological systems and their interactions. • Synthetic Biology: Design and construction of new biological systems. • Transcriptomics: Study of RNA expression and regulation. • Metagenomics: Study of microbial communities and their genomes. • Epigenomics: Study of gene regulation and epigenetic modifications. • Pharmacogenomics: Study of genetic variation and drug response.
- 8. Applications of BioinformationTechnologies • Personalized Medicine: Tailored medical treatment based on individual genetic profiles. • Gene Therapy: Treatment of genetic disorders through gene modification. • Cancer Research: Identification of cancer biomarkers and therapeutic targets. • Infectious Disease Research: Development of vaccines and therapies. • Agricultural Biotechnology: Improvement of crop yields and disease resistance. • Forensic Analysis: Use of DNA profiling in criminal investigations. • Gene Editing: Precise modification of genes using CRISPR/Cas9. • Synthetic Biology: Design of new biological pathways and organisms. • Biofuel Development: Microbial production of renewable energy sources. • Biodefense: Development of countermeasures against biological threats.
- 9. Tools and Software •BLAST: Sequence alignment and similarity search. • GenBank: Comprehensive DNA database. • UniProt: Protein database. • PDB: Protein structure database. • R: Statistical programming language. • Bioconductor: Open-source software for genomic analysis. • Bowtie: Short-read alignment software. • SAMtools: Sequence alignment and variant calling. • Cytoscape: Network visualization and analysis. • Genome Browser: Interactive genome visualization.
- 10. Semantic web andBioinformatics
- 11. Semantic Web • Thesemantic web is an extension of the traditional web, enabling machines to understand and interpret the meaning of data. • It's built on technologies like: Resource Description Framework (RDF) Ontologies (e.g., OWL) Linked Data • These technologies allow data to be structured, linked, and queried, facilitating machine reasoning and inference. • Bioinformatics is the application of computational tools and methods to analyze and interpret biological data.
- 12. Intersection of SemanticWeb and Bioinformatics The integration of semantic web technologies and bioinformatics enables: • Standardized data representation: Ontologies provide a common language for describing biological concepts and relationships. • Data integration: Linked data enables the connection of disparate biological datasets, facilitating comprehensive analysis. • Reasoning and inference: Semantic web technologies allow machines to infer new knowledge from existing data. • Improved data retrieval: SPARQL queries enable efficient retrieval of specific biological data.
- 13. Applications • Genome annotation:Semantic web technologies help annotate genomic regions with functional information. • Protein function prediction: Integrated data and reasoning enable prediction of protein functions. • Disease modeling: Semantic web-based models integrate data on disease mechanisms and pathways. • Personalized medicine: Integrated genomic and clinical data enable personalized treatment recommendations.
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- 15. • Genome projectsare scientific endeavors that aim to sequence, assemble, and analyze the complete genetic material (genome) of an organism. Types of Genome Projects: • Whole Genome Sequencing (WGS): Determining the complete DNA sequence of an organism's genome. • Exome Sequencing: Focusing on protein-coding regions (exons) of the genome. • Transcriptome Sequencing: Analyzing RNA transcripts to understand gene expression. • Epigenome Sequencing: Studying modifications to DNA or histone proteins.
- 16. Key Applications: • PersonalizedMedicine: Tailoring treatments based on individual genetic profiles. • Genetic Disease Diagnosis: Identifying genetic causes of diseases. • Crop Improvement: Developing disease-resistant and climate-resilient crops. • Synthetic Biology: Designing new biological pathways and organisms. • Forensic Analysis: Using genetic data for identification and crime solving.
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- 18. • Clinical informaticsis the application of informatics and information technology to improve healthcare delivery, advancing the quality, safety, and efficiency of patient care.
- 19. Key Components • HealthInformation Systems: Electronic Health Records (EHRs), Picture Archiving and Communication Systems (PACS), and Health Information Exchanges (HIEs). • Clinical Decision Support Systems: Providing healthcare professionals with real-time, evidence-based guidance. • Data Analytics: Analyzing healthcare data to identify trends, risks, and opportunities. • Telemedicine: Remote healthcare delivery using digital technologies. • Medical Imaging Informatics: Managing and analyzing medical images. • Pharmacoinformatics: Optimizing medication management. • Public Health Informatics: Applying informatics to population health.
- 20. Applications • Electronic HealthRecords (EHRs): Standardizing patient data. • Clinical Decision Support (CDS): Guiding diagnosis and treatment. • Computerized Physician Order Entry (CPOE): Reducing medication errors. • Telehealth: Expanding access to healthcare. • Predictive Analytics: Identifying high-risk patients. • Personalized Medicine: Tailoring treatments based on genetic profiles. • Disease Surveillance: Monitoring and responding to outbreaks.
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- 22. • Nursing informaticsis a specialty that integrates nursing science, computer science, and information science to design, develop, and implement information systems that support nursing practice, education, and research.
- 23. Scope • Clinical DecisionSupport: Developing systems to aid nurses in decision-making. • Electronic Health Records (EHRs): Designing and optimizing EHRs for nursing. • Telehealth: Expanding access to nursing care through digital technologies. • Data Analytics: Analyzing nursing data to improve patient outcomes. • Patient Engagement: Empowering patients through health information technology. • Nursing Education: Developing informatics-based educational tools. • Research: Conducting studies on nursing informatics innovations.
- 24. Key Concepts • StandardizedNursing Languages: Using standardized languages (e.g., SNOMED-CT) to document nursing care. • Nursing Taxonomies: Organizing nursing data using taxonomies (e.g., NANDA-I). • Health Information Exchange (HIE): Sharing patient data across healthcare settings. • User-Centered Design: Designing systems that meet nurses' needs. • Interoperability: Ensuring seamless data exchange between systems.
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- 26. Public Health Informatics(PHI) is the application of information technology and information systems to improve public health practice, research, and policy. It aims to prevent disease, promote health, and protect populations.
- 27. Scope • Surveillance :Monitoring and analyzing health data to identify trends and outbreaks. • Epidemiology : Investigating disease patterns and causes. • Health Education : Developing digital interventions to promote healthy behaviors. • Healthcare Access : Improving access to healthcare services. • Global Health : Addressing health disparities and inequities worldwide. • Emergency Preparedness : Responding to natural disasters and public health emergencies. • Environmental Health : Monitoring and mitigating environmental health hazards.
- 28. Key Concepts • HealthInformation Exchange (HIE) : Sharing data across healthcare settings. • Disease Surveillance Systems : Monitoring disease outbreaks (e.g., CDC's National Notifiable Diseases Surveillance System). • Geographic Information Systems (GIS) : Analyzing spatial health data. • Social Determinants of Health : Addressing non-medical factors influencing health. • Digital Health Equity : Ensuring equal access to digital health resources.