Public Health Strategies and Surveillance

SN6006 Information Technology in Healthcare

• Course title: SN6006 Information Technology in Healthcare
• Course subtitle: Bioinformatics and Public Health Informatics
• Lecturer: Dr. WK Chan ([email protected])
• Subject: Information Technology in Healthcare, Bioinformatics, Public Health Informatics

Introduction

• Bioinformatics: The biological application of information technology, focusing on data storage and analytics.
• Bioinformatics involves researching, developing, or applying computational tools and approaches to expand the use of biological, medical, behavioral, or health data. This includes acquiring, storing, organizing, archiving, analyzing, or visualizing this data.
• Computational Biology: The application of information technology in understanding biology, emphasizing analytical algorithms to analyze and understand biological processes.
• The field merges biology, computer science, and information technology to form a single discipline.

Interdisciplinary

• Bioinformatics development involves various scientific disciplines: biology, computer science, mathematics, statistics, physics, and chemistry
• Examples of areas in biology: biophysics, biochemistry, cell and molecular biology, genomics, and evolutionary biology.
• Examples of areas in computer science: programming, databases, data structures, machine learning, and artificial intelligence.
• Examples of areas in mathematics and statistics: biostatistics, probability theory, linear algebra, discrete mathematics, differential equations, Bayesian statistics, and calculus.

Required Skills

• Expertise in mathematical and statistical modeling
• Probability theory, graph theory, descriptive and inferential statistics, and differential equations.
• Computational skills to manage, store, and analyze large biological datasets using available algorithms and software.
• Statistical programming (e.g., R and Python)
• Knowledge of core biological subjects (e.g., genetics, genomics, biochemistry, molecular biology, and evolution)
• Knowledge and application of state-of-the-art technologies (e.g., next-generation sequencing and mass spectrometry)
• Computational thinking is different from computer programming. It's a logical thought process encompassing the formulation of complex problems and the possible computational solutions. Bioinformatics focuses on the ability to pose biological questions and find solutions via algorithmic thinking.

Computational Thinking

• Four steps: Decomposition, Pattern Recognition, Abstraction, Algorithms.
  • Decomposition: Breaking down complex problems into smaller, more manageable parts.
  • Pattern Recognition: Identifying similarities and patterns within those smaller parts.
  • Abstraction: Focusing on key information and identifying important underlying concepts.
  • Algorithms: Developing a systematic set of steps to solve the problem step by step

Translational Bioinformatics

• Involves development and use of computational methods to handle Biotechnology-generated data. This data is accumulated, assimilated, and analyzed to create new medical tools.
• Purpose is to optimize the transformation of voluminous biomedical data into proactive, predictive, preventive, and participatory (P4) medicine
• Focus on genomic, environmental, and clinical profiles to personalize medicine based on genomic data.
• The difference from bioinformatics is its focus on human health and directly translating biological discoveries into future or existing medicine.

Areas of Translational Bioinformatics

• Clinical Genomics: using patient genomes to inform clinical decision-making (developing biomarkers).
• Genomic Medicine: using genomic information in a patient’s care for personalized medicine.
• Pharmacogenomics: studying how genes affect responses to drugs, considering the patient's genetic material or genotype and its relationship with the drug target or phenotype.
• Genetic Epidemiology: aggregation (compiling) of genome-based data to analyze the relationship between genes and human health/disease and how genes interact with the environment.

Biotechnology

• Topics in Biotechnology: protein sequencing, evolutionary biology, molecular techniques, bioinformatic algorithms, DNA sequencing, and biological databases.

Protein Sequencing

• Edman degradation: method of purifying protein by sequentially removing one residue at a time.
• COMPROTEIN: Computer program designed to determine protein structure from amino acid sequences and developed the one-letter code in use today.
• Atlas of Protein Sequence and Structure: The First database constructed in 1965 by Dayhoff and colleagues.

Evolutionary Biology

• Molecular Clock: Evolutionary analysis of protein biomolecules (such as hemoglobin). An evolved sequence of protein (e.g., hemoglobin) is related to the evolutionary time of species (E.g., the fossil record). This observation suggests that the amount of difference in orthologous proteins is proportional to the evolutionary divergence between species.

Molecular Techniques

• Gene cloning - 1972 method that uses enzymes to cutout and insert a DNA fragment into the circular SV40 viral DNA. The resulting DNA is replicated and amplified, which yields large quantities of copies of the insert.
• Polymerase Chain Reaction (PCR): A laboratory technique to rapidly produce many copies of a specific DNA segment. It relies on primers, DNA polymerase, and dNTPs for rapid DNA replication.

Bioinformatics Algorithms

• Needleman-Wunsch Algorithm: An algorithm for aligning two protein sequences; one of the first applications of dynamic programming.
• Multiple sequence alignment (MSA): An extension of pairwise alignment, it's used to optimally match sequences.
• Feng-Doolittle progressive sequence alignment: A practical approach combining pairwise alignments to build a final MSA. Widely used computer software for MSA like Clustal.

Amino acid substitutions:

• Point accepted mutation (PAM): The replacement of a single amino acid in protein's primary structure by another single amino acid, which is accepted by natural selection processes as part of evolution.

DNA Sequencing

• Central Dogma: Specifies that the DNA sequence holds the information to create proteins.
• Plus and Minus method and Sanger method for DNA sequencing and their significance in sequencing history.
• Advantage of DNA sequencing: Allows identification and analysis of organisms' whole genome, leading to identification of expressed proteins, and prediction of protein structure through gene reading.

Human Genome Project (HGP)

• The development of DNA sequencing technology has paved the way for whole-genome sequencing of organisms.
• Started in 1990 and finished in 2003.
• DNA sequencing is now cheaper and faster.
• Annotation of information associated with diseases.
• Database searching and pattern matching.
• Large amounts of data now available to computational biologists.

Next-Generation Sequencing (NGS)

• Sanger sequencing was instrumental in the first human genome sequence.
• Next-Generation Sequencing (NGS) technologies were developed that provide a much higher throughput, quicker, and cheaper process for sequencing.
• Different generations of NGS technology (454, SOLiD, Illumina, Ion Torrent) and their significant roles in global health.

Biological Databases

• Classification of biological databases (comprehensive, specialized, primary, secondary) and their significant contribution to the field.
• Importance of sequence and structure databases, protein databases, and other biological databases.

Public Health Informatics

• Definition: The systematic application of informatics methods and tools to support public health goals.
• Focus on population prevention and public health policies.
• Public Health Informatics contrasts with Medical Informatics in focusing on the health of the community versus the individual patient.

Public Health Surveillance

• Definition, functions, and characteristics of public health surveillance.
• Disease surveillance, reportable/notifiable diseases, differences between active and passive disease surveillance.
• Syndromic surveillance: a specialized surveillance that identifies and tracks potential public health threats.

Privacy and Confidentiality

• Concepts of privacy, confidentiality, and authorization in the context of health information.
• Importance of legal and ethical principles in handling health data.

Public Health Ethics

• The ethical considerations, importance, and concerns in the use of health data in public health research and policy.

This quiz explores essential strategies for preventing diseases such as cervical cancer, effective public health surveillance, and the role of technology in public health practices. Test your understanding of evidence-based public health and the significance of data in shaping health programs.