I. section: Basics of bioinformatics

1.      Introduction to bioinformatics (Balázs Győrffy)

2.      Utilization of a training and test set (János Tibor Fekete)

3.      Statistical errors and dichotomania (János Tibor Fekete)

4.      Survival analysis* (Balázs Győrffy)

5.      ROC analysis: predicting sensitivity and specificity * (János Tibor Fekete)

II. section: Omics

6.      Similar genes and proteins, BLAST* (Balázs Győrffy)

7.      Introduction to genomics (Balázs Győrffy)

8.      Genomics: quality control*  (Ádám Nagy)

9.      Genomics: alignment of data to a reference genome * (Ádám Nagy)

10.  Genomics: identifying mutations (SNV, indels) (Ádám Nagy)

11.  Genomics: determining the consequence of a mutation* (Ádám Nagy)

12.  Genomics: what is the clinical relevance of a mutation, ClinVar, dbSNP*  (Ádám Nagy)

13.  Genomics: mutation signatures (Ádám Nagy)

14.  Genomics: copy number variations*  (Ádám Nagy)

15.  Genomics: identifying processing artefacts and quality issues (Otília Menyhárt)

16.  Proteomics: pre-processing (Balázs Győrffy)

17.  Transcriptomics: processing RNA-seq data (Balázs Győrffy)

18.  Proteomics: tools to analyze immunhistochemistry results (Gyöngyi Munkácsy)

19.  Proteomics: processing mass spectrometry (Áron Bartha)

20.  Proteomics: understanding molecular functions, Uniprot  (Gyöngyi Munkácsy)

21.  Genomics: GeneBank (Attila Marcell Szász)

III. section: Integrative science

22.  Application of multi-omic tools (Otília Menyhárt)

23.  Clinical studies utilizing multi-omics (Otília Menyhárt)

24.  Multiple hypothesis testing* (Balázs Győrffy)

25.  Analyzing COVID-19 (Gyöngyi Munkácsy)

26.  Reproducibility issues in medicine (Otília Menyhárt)

IV. section: Artificial intelligence

27.  Introduction to artificial intelligence (Balázs Győrffy)

28.  Machine learning* (János Tibor Fekete)

29.  The Bayes rule (János Tibor Fekete)

30.  Clinical application of a decision tree (Áron Bartha)

31.  Determining distance* (Balázs Győrffy)

32.  Clustering* (Balázs Győrffy)

33.  Neuronal networks* (Balázs Győrffy)

34.  Principal component analysis (Áron Bartha)

35.  Support Vector Machines (János Tibor Fekete)

36.  Regression* (Áron Bartha)

37.  Diagnostic tools using artificial intelligence (Attila Marcell Szász)

V. section: Medical informatics

38.  Using REDcap (Attila Marcell Szász)

39.  Electronic health records (Áron Bartha)

40.  Time distortion and computer addiction (Otília Menyhárt)

41.  Development, learning and work (Otília Menyhárt)

42.  Outlook(Balázs Győrffy)

 

Each lecture comprises of three 15-minute talks. Starred lectures have associated exercises.

 

Planned schedule for the lectures and exercises:

 

Week

Lecture

Exercise

1.

1, 3, 15

1

2.

7, 8, 16

7

3.

9, 10, 18

8

4.

2, 27, 5

3

5.

4, 17, 19

5

6.

6, 20, 29

9

7.

21, 25, 30

10

8.

11, 12, 36

11

9.

13, 14, 33

12

10.

22, 26, 31

17

11.

23, 28, 34

27

12.

24, 32, 35

31

13.

37, 40, 41

32

14.

38, 42, 39

consultation

 

The exercises are held by: Balázs Győrffy, Áron Bartha, János Tibor Fekete, Máté Balajti, Ádám Nagy

 

It is recommended to bring a private laptop for the exercises.