Mid.presentation.pdf

Introduction to Bioinformatics Bioinformatics is the interdisciplinary ﬁeld that combines biology, and information technology to analyze and interpret biological data. It is a powerful tool for understanding the complex processes of life and driving advancements in ﬁelds like Genomics Proteomics Personalized medicine. by Mohammad Adnan Khalil What is Bioinformatics? Data Management Computational Interdisciplinary 1 Bioinformatics focuses on the Analysis It utilizes advanced algorithms Approach Bioinformatics integrates eﬃcient storage, organization, and statistical methods to principles and techniques from 2 3 and retrieval of large biological extract meaningful insights various ﬁelds, including biology, datasets, such as DNA from biological data, enabling computer science, sequences, protein structures, researchers to uncover mathematics, and engineering, and gene expression data. patterns, make predictions, and to address complex biological draw conclusions. problems. Applications of Bioinformatics Genomics Drug Discovery Personalized Bioinformatics enables the analysis It assists in the identiﬁcation of Medicine Bioinformatics supports the of DNA sequences, gene potential drug targets, virtual development of personalized identiﬁcation, and the study of screening of chemical compounds, treatment approaches by analyzing genetic variations, which is crucial and the prediction of drug-target an individual's genetic proﬁle and for understanding the genetic basis interactions, accelerating the drug identifying speciﬁc biomarkers that of diseases and developing discovery process. can guide clinical decision-making. targeted therapies. Biological Data and Databases DNA Sequences Protein Structures Bioinformatics databases store These databases also host a and manage vast collections of wealth of data on protein DNA sequence data, enabling structures and functions, which researchers to access and analyze are crucial for understanding genetic information. biological processes and drug development. Gene Expression Metabolic Pathways Data Bioinformatics tools enable the Bioinformatics databases also storage, retrieval, and analysis of contain information on metabolic gene expression data, which is pathways and interactions, aiding essential for studying gene the understanding of cellular regulation and disease processes and the development of mechanisms. targeted therapies. Benefits gained through Bioinformatics Analysis: The OMICS ▪ Genomics (DNA Analysis: Genomics & Mitochondrial) ▪ Transcriptomics (Gene Products: mRNA, ▪ Messenger RNA (mRNA) mRNA accounts for just 5% of the total RNA in the cell ▪ Ribosomal RNA (rRNA) ▪ Transfer RNA (tRNA) ▪ Small nuclear RNA (snRNA) ▪ Regulatory RNAs ▪ Transfer-messenger RNA (tmRNA) ▪ Ribozymes (RNA enzymes) ▪ Double-stranded RNA (dsRNA) Benefits gained through Bioinformatics Analysis: The OMICS ▪ Proteomics (Peptides, proteins, Protein folding, Post Translational Modifications, Packaging and transport, Exocytosis products, Proteins turnover…etc) ▪ Metabolomics (cellular metabolic pathways; e.g. Glycolysis, Krebs Cycle…etc.) ▪ Metabolome: represents the collection of all metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. ▪ Lipidomics (fatty acid synthesis and degradation, packaging and transport, cholesterol synthesis, transport and degradation…etc) Home Work (1) What is: Omics AI Sequence Alignment Techniques Global Alignment Multiple Sequence Compares entire sequences to identify similarities and Alignment Aligns multiple sequences simultaneously to uncover diﬀerences across the entire length of the molecules. conserved regions and evolutionary relationships. 1 2 3 https://www.sequence-alignment.com/ Local Alignment Identiﬁes regions of high similarity within longer sequences, even if the overall sequences are not closely related. Phylogenetic Analysis Evolutionar y Bioinformatics tools enable the construction of phylogenetic Relationshi trees that depict the evolutionary relationships between diﬀerent ps species or organisms. Genetic Similarity These analyses rely on the comparison of genetic sequences to infer the degree of relatedness between organisms and trace their evolutionary history. Comprehen sive Bioinformatics databases provide the vast amounts of sequence Databases data necessary for conducting comprehensive phylogenetic studies and understanding the tree of life. Protein Structure Prediction Primary Structure Bioinformatics techniques analyze the linear sequence of amino acids that make up a protein. Secondary Structure Computational models are used to predict the local folding patterns of the protein, such as alpha helices and beta sheets. Tertiary Structure Advanced algorithms simulate the overall 3D shape and spatial arrangement of the protein based on its primary and secondary structures. Quaternary Structure Bioinformatics can also predict how multiple protein subunits assemble into a larger, functional complex. Emerging Trends in Bioinformatics Machine Learning Leveraging advanced algorithms and neural networks to enhance the accuracy and eﬃciency of bioinformatics analysis. Integrative Omics Combining multiple "omics" datasets, such as genomics, transcriptomics, and proteomics, to gain a more comprehensive understanding of biological systems. Single-Cell Analysis Analyzing genetic and molecular proﬁles at the individual cell level to uncover cellular heterogeneity and identify rare cell types. Precision Medicine Using bioinformatics to personalize medical treatments and interventions based on an individual's unique genetic and molecular proﬁle. Home Work (2) ❑ Review Molecular Biology Basics (Transcription, Translation, Genome, nucleolus, epigenome, Post-Translational Modification, nuclear structure, cellular location of gene expression, Primary, Secondary, Tertiary, and Quaternary structure of proteins…etc. ❑ Define the following terms that we discussed thus far, pertaining to Bioinformatics and Health informatics. All responses should be submitted handwritten for this assignment; a legible photo or scan is sufficient. Medical Informatics gy Terminolo Bioinformatics Genomics Data Clinical Informatics Translational Genome vs. Informatics Information Exome Gene Expression Data Gene Sequence Alignment Individual Genetic Profile DNA Sequence CoreTTex EPIC Metabolic Pathway Gene Biomarkers Identification Big DATA Phylogenetic Analysis Personalized Genetic Protein Structure Medicine Variation Prediction Machine Learning Virtual Targeted Screening of Therapy drug-targets Integrative Omics Drug Discovery Single-Cell Analysis Terminology Phylogenetic Analysis Cont’d… Protein Structure Prediction Medical Informatics Machine Learning Clinical Informatics Translational Informatics Integrative Omics Gene Expression Data Single-Cell Analysis Gene Sequence Alignment Clinical Decision Support System Metabolic Pathway Telehealth Big DATA Disease Surveillance Healthcare data analytics Introduction to Medical Informatics Medical Informatics is the interdisciplinary ﬁeld that combines, information science, and technology to improve patient care, streamline clinical workﬂows, and drive medical innovation. It encompasses the use of: 1) Digital tools 2) Data-driven insights For the purpose of: 1) Enhancing decision-making 2) Promoting preventive care 3) Improve the delivery of healthcare services Deﬁnition and Scope Deﬁnition Scope Interdisciplinary Application of information + 1) Data and Information Medical Informatics integrates the communication technologies to Management working knowledge from various the ﬁeld of healthcare 2) Clinical decision support disciplines including: systems Computer science Objectives: 3) Telehealth Information science Improve patient outcomes 4) Disease surveillance Medicine Reduce costs 5) Healthcare data analytics Nursing Enhance the overall Public health efficiency of the healthcare system. Scope: Data and Information Management 1 Electronic Health Comprehensive Records digital platforms that store and (EHRs) manage patient data (e.g. EPIC, CorTTex…etc.), enabling seamless access and exchange of information across healthcare providers. Scope: Data and Information Management Health 2 Information Secure systems that facilitate the sharing of medical Exchange (HIE) information between healthcare organizations, improving care coordination and patient outcomes. Scope: Data and Information Management Data 3 Analyti The cs use of advanced data analysis techniques to extract insights from healthcare data, supporting clinical decision-making and population health management. Scope: Data and Information Privacy 4Management and Robust measures to protect the conﬁdentiality and integrity Securit y of patient data, ensuring compliance with regulatory requirements and maintaining public trust. Scope (2): Clinical Decision Support Systems Knowledge-based Systems Expert systems that leverage clinical guidelines, rules, and algorithms to personalized treatment recommendations and alert clinicians to potential issues. Scope (2): Clinical Decision Support Systems Predictive Analytics Advanced machine learning models that analyze patient data to predict disease risk, identify high-risk individuals, and suggest preventive interventions. Scope (2): Clinical Decision Support Systems Workﬂow Integration Clinical decision support systems seamlessly integrated into the electronic health record, guiding healthcare professionals through the care delivery process. Scope (2): Clinical Decision Support Systems Improved Outcomes Clinical decision support systems have been shown to enhance the quality of care, reduce medical errors, and improve patient safety and clinical outcomes. Scope (3) Telemedicine and eHealth Telehealth eHealth Virtual consultations and The use of digital tools and remote patient monitoring, technologies to support various leveraging video conferencing, healthcare functions, such as mobile apps, and connected electronic health records, devices to deliver care at a patient engagement, and distance. population health management. Connected Patient Care The integration of digital Empowerme Telemedicine and eHealth technologies, including nt empower patients to take a wearable devices and remote more active role in managing monitoring tools, to enable their health, leading to continuous, personalized improved engagement and healthcare delivery. better outcomes. Challenges and Ethical Considerations Interopera bility Ensuring seamless data exchange and integration between different healthcare IT systems and platforms. Data Privacy and Security Safeguarding patient information and maintaining compliance with data protection regulations. Workforce Readiness Upskilling healthcare professionals to effectively leverage medical informatics tools and technologies. Ethical Considerati Addressing issues related to patient consent, data ons ownership, and the responsible use of AI and analytics in healthcare. Future Trends and Innovations Precision Medicine Tailoring healthcare interventions to the individual based on genetic, lifestyle, and environmental Artiﬁcial Intelligence factors. The use of AI and machine learning to enhance clinical decision-making, improve disease prediction, and automate administrative tasks. Blockchain Technology Secure and decentralized platforms for managing electronic health records and enabling trusted data exchange. Augmented and Virtual Reality Immersive technologies for medical training, remote collaboration, and enhanced patient engagement. Introduction to Clinical Informatics Clinical informatics is the interdisciplinary ﬁeld that focuses on the effective use of information and technology to improve healthcare delivery and patient outcomes. It combines medical expertise with data analysis and technology to enhance clinical decision-making and optimize healthcare systems. ‫ل‬ Deﬁnition and Scope Data-Driven Multidisciplinary Improved Outcomes 1 2 3 Approach Clinical informatics leverages Collaboration The goal is to enhance patient data and information to It involves collaboration between care, increase efficiency, and support clinical practice, healthcare professionals (e.g. optimize healthcare delivery. research, and administration. Physicians, nurses, technologists, (Evidence-Based Medicine administrators, physical therapists, (EBM) radiologists…etc.), computer scientists, and information History and Evolution 1960s 1 Early adoption of computers in medical settings for record-keeping and data management. 1980s 2 Emergence of electronic health records (EHRs) and decision support systems. 2000s 3 Rapid growth of mobile technologies, telehealth, and data-driven healthcare innovations. SCOPE: Data and Information Management Data Collection Data Analysis Information Capturing and organizing Applying Dissemination Presenting data-driven clinical data from various 1) Statistical Methods (e.g. insights to healthcare sources, including ANOVA and MANOVA) providers, administrators, 1) Electronic Health 2) Machine Learning and patients to support Records techniques to extract informed decision-making. 2) Medical devices insights and patterns 3) Patient-reported from healthcare data information (Medical History) SCOPE: Clinical Decision Support Systems Knowledge-Bas Predictive ed Systems Providing healthcare Analytics Leveraging machine learning professionals with models to anticipate patient evidence-based outcomes, predict disease recommendations and risks, and suggest personalized guidelines to improve interventions. Automated diagnosis and treatment. Personalized Alerts potential issues, Detecting Care Tailoring clinical decisions and such as drug interactions or treatment plans based on abnormal test results, and individual patient generating timely notiﬁcations characteristics, preferences, to prevent adverse events. and genomic data. Up-to-Date Telemedicine and Telehealth Remote Consultation Enabling healthcare providers to connect with spatients remotely through video conferencing and other technologies. Remote Monitoring Allowing patients to transmit health data, such as vital signs and symptoms, to their healthcare team for continuous monitoring. Virtual Care Coordination Facilitating communication and collaboration between healthcare providers, patients, and caregivers to coordinate care plans and manage chronic conditions. Ethical and Legal Considerations Privacy Security Ensuring the conﬁdentiality Implementing robust and secure handling of patient cybersecurity measures to data. protect against data breaches and unauthorized access. Equity Regulation Ensuring equitable access to Complying with relevant laws digital healthcare technologies and regulations governing and addressing disparities. healthcare technology and data usage. Emerging Trends and Future Directions Artiﬁcial Intelligence Leveraging AI and machine learning to enhance clinical decision-making and automate Genomics and Precision certain tasks. Integrating genomic data and Medicine personalized insights to deliver tailored treatments and preventive care. Wearable Devices Utilizing wearable technologies to enable continuous health monitoring and remote patient engagement. Virtual and Augmented Applying immersive Reality technologies for medical training, remote procedures, and patient education. Big DATA ▪ Big data describes large and diverse datasets that are: Extremely large and diverse collections of Structured (Excel worksheets) Unstructured (X-Ray, Ultrasound, CT scan, MRI) Semi-structured data (Log files) Rapidly growing in size over time ▪ Big data applications ▪ Machine learning (Cancer Prognosis; Aging Population Progressive diseases…etc.) ▪ Predictive modelling (Pandemic spreading. ▪ Career Planing ▪ Challenges using Big DATA ▪ Data storage format ▪ Data Processing Generati ve AI Generative AI uses models to create novel protein sequences with specific properties for designing antibodies, enzymes, vaccines, and gene therapy. Healthcare and life sciences companies can use generative models to design synthetic gene sequences for applications in synthetic biology and metabolic engineering. Like all artificial intelligence, generative AI works by using machine learning models—very large models that are pre-trained on vast amounts of data. Traditional AI vs. Generative AI Traditional AI excels at analyzing data and performing specific tasks Generative AI focuses on creating new content like text, images and graphs Examples Chat GPT Generative AI on Google Cloud DALL-E 2 Free Technology S. No. Tool Name Uses Version Used Available Generative Chatbot, 1 ChatGPT Pre-trained Content Yes Transformer Generation Text, Audio, Generative Image, 2 GPT-4 Pre-trained Video, 3D Yes Transformer Model Generation Comparison – Generative AI Tools 3 GitHub Generative Pre-trained Code No Here is a table that compares AI-generative Copilot Generation Transformer programs, looking at their purposes, the Large Coding 4 AlphaCode Language Assistance Yes technologies they involve, and if there’s an option Model (LLM) Natural to use them for free. 5 Bard Language Processing Chatbot Yes and Machine Learning Langchain Cohere Technologies Content 6 Generate and Helsinki Generation Yes NLP models Innovative Content 7 Claude Constitutiona Generation, No l AI AI Assistant Neural Video Video 8 Synthesia Synthesis Creation No Image and Diffusion 9 DALL.E – 2 Model Art No Medical Informatics gy Terminolo Bioinformatics Genomics Data Clinical Informatics Translational Genome vs. Informatics Information Exome Gene Expression Data Gene Sequence Alignment Individual Genetic Profile DNA Sequence CoreTTex EPIC Metabolic Pathway Gene Biomarkers Identification Big DATA Phylogenetic Analysis Personalized Genetic Protein Structure Medicine Variation Prediction Machine Learning Virtual Targeted Screening of Therapy drug-targets Integrative Omics Drug Discovery Single-Cell Analysis Terminology Phylogenetic Analysis Cont’d… Protein Structure Prediction Medical Informatics Machine Learning Clinical Informatics Translational Informatics Integrative Omics Gene Expression Data Single-Cell Analysis Gene Sequence Alignment Clinical Decision Support System Metabolic Pathway Telehealth Big DATA Disease Surveillance Healthcare data analytics Electronic Health Health Information Data Analytics Records Exchange (HIE) (EHRs) CoreTTex EPIC Privacy and Knowledge-ba Predictive Security sed Systems Analytics Telehealth eHealth Workflow (remote (Population Integration Consultation) Monitoring) Connected Artificial Blockchain Care Intelligence Technology Augmented Generative and Virtual Reality AI Resources (1) ▪ What is Bioinformatics? ▪ AI-powered Drug Discovery lecture by Dr. Michael Levitt, 2013 ▪ https://www.youtube.com/watch?v=W-Ov2cUaYQY Nobel Laureate in Chemistry ▪ https://www.youtube.com/watch?v=HbfFS7bA5M0 ▪ What is Health Informatics? ▪ https://www.youtube.com/watch?v=Uh0ofe5z5po ▪ Revolutionizing drug discovery with artiﬁcial intelligence ▪ https://www.youtube.com/watch?v=EW9B43cs6Yc ▪ What is Biomedical Informatics? ▪ https://www.youtube.com/watch?v=FPmUrcmWv9I ▪ The Drug Development Process in Pharma ▪ https://www.youtube.com/watch?v=vT2Q6ZWVBnk ▪ Genetics for beginners | Genes Alleles Loci on Chromosomes | ▪ https://www.youtube.com/watch?v=wpraShKAJcw ▪ Bioinformatics & Drug Discovery - Must Watch For All Research Enthusiasts ▪ David Baker (U. Washington / HHMI) Part 1: Introduction to ▪ https://www.youtube.com/watch?v=z2XvrbRw7y8 Protein Design ▪ https://www.youtube.com/watch?v=0LetJMbu7uY&list=PLo1Ke4tmyY ▪ What is Health Informatics? MBQHiiTv8ojUeB6Qal4B5Gl&index=17 ▪ https://www.youtube.com/watch?v=fpekSZdJHKQ ▪ Basics of Exome Sequencing | Genetics 101 | Ambry Genetics ▪ Isaac Kohane | Why Biomedical Informatics? ▪ https://www.youtube.com/watch?v=-4zEwtAh-I0&list=PLo1Ke4tmyYM ▪ https://www.youtube.com/watch?v=XCTL8TmqiPM BQHiiTv8ojUeB6Qal4B5Gl&index=51 Resources (2) Bio Nanotechnology mRNA Vaccines _Your Future In Nano https://www.youtube.com/watch?v=-r6gz2_BP34 The Human Genome Project | Genetics | Biology | FuseSchool https://www.youtube.com/watch?v=-hryHoTIHak Lessons from the Human Genome Project https://www.youtube.com/watch?v=qOW5e4BgEa4 Whole Genome Sequencing: What Can You Expect? https://www.youtube.com/watch?v=7_POT6VuJ8Y Human Genome Project Explained https://www.youtube.com/watch?v=DaN5ky3ygAo Structure, Function and Types of RNA (mRNA, tRNA, rRNA,lncRNA, miRNA, siRNA, snoRNA, snRNA, piRNA) https://www.youtube.com/watch?v=FThA4Vxs3v4&list=PLo1Ke4tmyYMBQHiiTv8ojUeB6Qal4B5Gl&index=55 AI and Clinical Practice—Discovery and Scaling Findings From Large, Multicenter Health Care Datasets https://www.youtube.com/watch?v=kBPxcAoAhPg&list=PLo1Ke4tmyYMAOtZAhOio2oKd_N9ABqabf&index=1&t=844s Top 7 AI Examples In Healthcare - The Medical Futurist https://www.youtube.com/watch?v=mkiDXTS6-mU

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