The topics given in the thematic guide are 15-minute lectures.


I. blokk: Basics of Bioinformatics

  1. Introduction to bioinformatics ( Dr. Balázs Győrffy)
  2. Utilization of a training and test set (Dr. János Tibor Fekete)
  3. Statistical errors and dichotomania (Dr. János Tibor Fekete)
  4. Survival analysis: Cox regression and the Kaplan-Meier plot (Prof. Dr. Balázs Győrffy)
  5. ROC analysis: predicting sensitivity and specificity (Dr. János Tibor Fekete)
  6. Correlation (Dr. Otília Menyhárt)
  7. Multiple hypothesis testing (Prof. Dr. Balázs Győrffy)
  8. Applications of bioinformatics and artificial intelligence (Prof. Dr. Balázs Győrffy)

Block I theoretical hours: 2,7 hours (120 minutes)

II. blokk: Omics

  1. Introduction to genomics (Prof. Dr. Balázs Győrffy)
  2. Similar genes and proteins, BLAST (Prof. Dr. Balázs Győrffy)
  3. Genomics: quality control (Dr. Gyöngyi Munkácsy)
  4. Genomics: alignment of data to a reference genome (Dr. Gyöngyi Munkácsy)
  5. Genomics: identifying mutations (SNV, indels) in normal and tumor samples (Dr. Gyöngyi Munkácsy)
  6. Genomics: determining the consequence of a mutation (Dr. Gyöngyi Munkácsy)
  7. Genomics: what is the clinical relevance of a mutation, ClinVar, dbSNP (Dr. Gyöngyi Munkácsy)
  8. Genomics: copy number variations (Dr. Gyöngyi Munkácsy)
  9. Genomics: identifying processing artefacts and quality issues (Dr. Otília Menyhárt)
  10. Transcriptomics: processing RNA-seq data (Prof. Dr. Balázs Győrffy)
  11. Processing single-cell data (Prof. Dr. Balázs Győrffy)
  12. Genomics: GeneBank and the database of genes (Dr. Attila Marcell Szász)

Block II theoretical hours: 4 hours (180 minutes)

III. blokk: Proteomics

  1. Proteomics and transcriptomics: pre-processing (Prof. Dr. Balázs Győrffy)
  2. Proteomics: tools to analyze immunhistochemistry results (Dr. Gyöngyi Munkácsy)
  3. Proteomics: processing mass spectrometry (Dr. Áron Bartha)
  4. Proteomics: advantages and limitations of mass spectrometry (Dr. Áron Bartha)
  5. Protein structure determination: physical/chemical methods (Dr. Gábor Tusnády)
  6. Modelling protein structure: from simple (homology model) to complex (ab initio) (Dr. Gábor Tusnády)
  7. Modelling the structure of transmembrane proteins (Dr. Gábor Tusnády)
  8. Proteomics: understanding molecular functions, Uniprot (Dr. Gyöngyi Munkácsy)
  9. Signal transduction, KEGG (Dr. Attila Marcell Szász)
  10. Graphical comparison of two groups: boxplot, violin plot, density plot, heatmap, correlation, matrix (Dr. Áron Bartha)

Block III theoretical hours: 3.3 hours (150 minutes)

IV. blokk: Artificial intelligence

  1. Machine learning (Dr. János Tibor Fekete)
  2. The Bayes rule (Dr. János Tibor Fekete)
  3. Principal component analysis (Dr. Áron Bartha)
  4. Determining distance (Prof. Dr. Balázs Győrffy)
  5. Clustering (Prof. Dr. Balázs Győrffy)
  6. Neural networks (Prof. Dr. Balázs Győrffy)
  7. Convolutional neural networks (CNN) (Dr. Otília Menyhárt)
  8. Deep learning (Dr. Otília Menyhárt)
  9. Clinical application of a decision tree (Dr. Áron Bartha)
  10. Feature selection (Dr. Otília Menyhárt)
  11. Support Vector Machines (Dr. János Tibor Fekete)
  12. Regression (Dr. Otília Menyhárt)
  13. The general pre-trained transformer (Dr. János Tibor Fekete)

Block IV theoretical hours: 4.3 hours (195 minutes)

V. blokk: Integrative sciences

  1. Multi-omics (Dr. Otília Menyhárt)
  2. Gene ontology (Dr. János Tibor Fekete)
  3. Reproducibility issues in medical research (Dr. Otília Menyhárt)
  4. Evaluation of Chip-seq and ATAC-seq data (Dr. Bálint Bálint)
  5. Evaluation of DNA methylation data (Dr. Bálint Bálint)
  6. Epigenetic databases (Dr. Bálint Bálint)
  7. Using Excel for database management (Dr. Gyöngyi Munkácsy)
  8. Data mining: Excel, PubMed, Watson (Dr. Gyöngyi Munkácsy)
  9. Using REDcap (Dr. Attila Marcell Szász)
  10. Time distortion and computer addiction (Dr. Otília Menyhárt)
  11. Blockchain and data security (Dr. Áron Bartha)

Block V theoretical hours: 3.7 hours (165 minutes)


Practical training:

I. blokk: Basics of Bioinformatics

  1. Survival analysis: Cox regression and the Kaplan-Meier plot, 2 hours ( Dr. Balázs Győrffy)
  2. ROC analysis, 2 hours (Dr. János Tibor Fekete)
  3. Multiple hypothesis testing, 2 hours (Prof. Dr. Balázs Győrffy)
  4. Introduction to Galaxy, 1 hour (Dr. Otília Menyhárt)

Block I Practical hours: 7 hours (315 minutes)

II. blokk: Genomics and transcriptomics

  1. Quality control, 2 hours (Dr. Gyöngyi Munkácsy)
  2. Alignment of data to a reference genome, 2 hours (Dr. Gyöngyi Munkácsy)
  3. Identifying variants, 2 hours (Szonja Anna Kovács)
  4. Consequences of mutations, 2 hours (Dr. Gyöngyi Munkácsy)
  5. Transcriptomics: processing RNA-seq data, 2 hours (Prof. Dr. Balázs Győrffy)

Block II Practical hours: 10 hours (450 minutes)


IV. blokk: Artificial intelligence

  1. Clustering, 1 hour (Dr. Otília Menyhárt)
  2. Classification, 2 hours (Dr. János Tibor Fekete)
  3. Regression, 1 hour, (Dr. Otília Menyhárt)
  4. Deep learning, 2 hours (Dr. János Tibor Fekete)

Block IV Practical hours: 6 hours (270 minutes)


V. blokk: Integrative science

  1. Epigenetics, 1 hour (Dr. Bálint Bálint)

Block V Practical hours: 1 hour (45 minutes)


A consultation is possible during the last practical session.

Total number of theoretical hours: 18 hours (810 minutes)

Total number of practical hours: 24 hours (1080 minutes)


A tömbösített napi beosztás tematikai elosztással (témaszámok a fenti lista alapján):


Theory covered

Practical topics

Total hours



55, 56, 57



9-14, 28-30

58, 59, 60