Introduction to Biomedical Data Analysis in Medical Research

Could you change the title to this Module / unit title: Introduction to Bioinformatics 5 ECTS / EQF 6 The title should reflect a broader application in medicine research, as Bioinformatics can give the impression that this is focused on bioinformatics used in genetics. This unit can include some bioinformatics , as well as a basic statistical theory (ex. Sampling techniques , hypothesis testing, confidence intervals etc.. ).

Could you develop the module/unit and the reading list according with the new title

Module/Unit Title: Introduction to Biomedical Data Analysis 5 ECTS / EQF 6

Module/Unit Description:

This module provides a comprehensive introduction to the principles and applications of data analysis in biomedical research. It covers essential bioinformatics tools and techniques, as well as fundamental statistical theories and methods. Students will learn how to handle and interpret biomedical data, including genetic data, clinical trial data, and other types of health-related data. The course aims to equip students with the skills necessary to apply data analysis in various medical research contexts.

Learning Outcomes:

By the end of this module, students will be able to:

1. Understand the basic concepts and applications of bioinformatics in biomedical research.
2. Apply statistical techniques such as sampling methods, hypothesis testing, and confidence intervals to biomedical data.
3. Use bioinformatics tools to analyze genetic and other types of biomedical data.
4. Interpret and critically evaluate the results of biomedical data analyses.
5. Communicate findings effectively in both written and oral formats.

Module/Unit Content:

1. Introduction to Biomedical Data Analysis

   • Overview of biomedical data types
   • Importance of data analysis in medical research

2. Basic Bioinformatics

   • Introduction to bioinformatics tools and databases
   • Sequence alignment and analysis
   • Genomic data interpretation

3. Statistical Theory and Methods

   • Basic concepts in statistics
   • Sampling techniques
   • Descriptive statistics
   • Probability distributions

4. Hypothesis Testing

   • Null and alternative hypotheses
   • Types of errors
   • p-values and significance levels
   • Common statistical tests (t-tests, chi-square tests, etc.)

5. Confidence Intervals

   • Concept and interpretation
   • Calculation of confidence intervals for different data types

6. Data Analysis in Biomedical Research

   • Case studies and practical applications
   • Data visualization techniques
   • Ethical considerations in biomedical data analysis

7. Practical Sessions

   • Hands-on exercises with bioinformatics tools
   • Statistical analysis using software (e.g., R, Python)

Assessment Methods:

• Assignments: Practical exercises and problem sets (30%)
• Midterm Exam: Written exam covering theoretical concepts (20%)
• Project: Data analysis project with a written report and presentation (50%)

Reading List:

1. Primary Textbooks:

   • "Bioinformatics: Sequence and Genome Analysis" by David W. Mount
   • "Introduction to the Practice of Statistics" by David S. Moore, George P. McCabe, and Bruce A. Craig

2. Supplementary Books:

   • "Biostatistics: A Foundation for Analysis in the Health Sciences" by Wayne W. Daniel and Chad L. Cross
   • "Practical Statistics for Medical Research" by Douglas G. Altman
   • "R for Data Science" by Hadley Wickham and Garrett Grolemund

3. Journal Articles and Papers:

   • Selected articles from journals such as Bioinformatics, Journal of Biomedical Informatics, and Statistics in Medicine

4. Online Resources:

   • NCBI (National Center for Biotechnology Information) tutorials and resources
   • Online courses and tutorials on statistical software (e.g., Coursera, edX)

Additional Resources:

• Access to bioinformatics software and databases
• Statistical analysis software (e.g., R, Python)
• Online forums and study groups for peer support

This module/unit is designed to provide a solid foundation in biomedical data analysis, preparing students for further studies or careers in medical research and related fields.