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Biomedical Data School

A practical introduction to bioinformatics and biomedical data analysis for researchers working with genomic and clinical data. We will provide an end-to-end analysis steps, guiding participants from raw data all the way to the interpretation of results across different case study scenarios. By Codebridge College.
Target audience: PhD students, researchers, doctors and professionals in biomedical sciences, medicine, pharmacy, clinical research, and healthcare
Prerequisites: Intermediate Excel, basic Python/R
Date: Dec 8 - 12, 2025 Time: 5 full-time days, 8:30 – 17:30 Place: Bratislava
Language: Slovak

Course Structure

Day 1 – Intro to Bioinformatics

Fundamentals of Biomedical Data
Fundamentals of Healthcare Data
Bioinformatics Basics (databases, sequencing terminology, tools)
Hands-on workshops
Terminal intro (awk, command line)
Python recap (Pandas, DataViz, Biopython)
Sequence alignment & Quality Control (formats: FASTA, SAM, GTF, BED)
Variant calling (SNPs, indels, CNVs)

Day 2 – Biostatistics and Survival Models

Intro to Statistical Epidemiology Biostatistics
Hypothesis testing Parametric vs non-Parametric tests Regression Models
Survival analysis
Kaplan-Meier, Censoring
Cox proportional hazards models
Advanced survival model, Time-dependent Cox Competing risks
Forest plots, hazard ratios

Day 3 – Transcriptomics and Gene Expression

Understanding Transcriptomics From raw reads to expression counts Analysing Gene Expression Data Exploratory analysis (PCA, clustering, batch effects)
Practical RNA-Seq Filtering, DESeq2 Differential expression Gene Ontology
Advanced topics
Mislabeling detection Batch correction Intro to single-cell seq

Day 4 – Bioinformatics & Precision Medicine

Metagenomics
Oxford Nanopore Theory Clinical relevance Practical workshop
Biostatistics Transcriptional noise Multiple testing correction Mutation Analysis
Gene Expression & Tumour Subtyping Survival Analysis & Prognostic Biomarkers

Day 5 – Mini Hackathon

Format: Team projects on biomedical datasets
Goal: use new skills to answer a real-world research question
Presentations & feedback session

Lecturers

Viera Kováčová Bioinformatician with 15 years of experience in genomics, transcriptomics, metagenomics, phylogenetic reconstruction and host–pathogen interactions. She specializes in transforming raw sequencing data into meaningful biological insights, with a strong focus on transcriptome analysis, variant calling, and protein–protein interaction prediction. Veve has extensive experience across R, Python, Julia, Shiny, Bash, AWS, and HPC environments. She is currently Senior Research Associate at University of Cologne and previously served as Head of Bioinformatics.
Imrich Berta Applied mathematics (Computational Biology) graduate from University of Cambridge, experienced in machine learning models for disease prediction, biostatistics and clinical data analysis. Currently works as a consultant for government on health data, cancer epidemiology and public health. Actively mentors analysts and organizes coding workshops for students. Imrich enjoys helping people who don’t see themselves as “numbers people” put mathematical and statistical concepts into practice.
Laura Johanesová Bioinformatician and biomedical scientist currently at the University of Vienna. The skills she has in shell scripting, R and Python are crucial for her research in regeneration. She designs and teaches intensive, practice-first trainings that help analysts move from Excel to Python/R and applied machine learning. Laura’s work spans curriculum design, data upskilling in science, and various projects in healthcare and biomedical data.

Price

Standard
Private €1,100 | Public €950 | Academia €800
Early registration (until Oct 20) Private €1,000 | Public €850 | Academia €700
All prices are VAT excluded.

Contact

Laura Johanesová [email protected]

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