1. BioInformatics and
Information Technology
research group
https://biit.cs.ut.ee/

3. Statistical Analysis

4. Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?

5. Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?
Hypothesis testing
p < 0.05

6. Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?
1
2345
Correction for multiple testingHypothesis testing
p < 0.05

7. Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?
1
2345
Correction for multiple testingHypothesis testing
p < 0.05
Amazing discovery by Estonian
scientists
Figure 1. Protoarray reactivities to type I interferons (IFNs) and to known autoantigens. The
reactivities to type I IFNs and known APECED autoantigens. The Protoarray signals are
expressed as z-scores representing the number of SDs from the mean of combined control
samples. Positive-negative discrimination level (dark red line) was set at z = 3. Red circles
represent the samples with z > 3, gray circles are control samples.
Presenting findings

8. Machine
Learning

9. Machine
Learning
Cluster
analysis
Supervised
analysis

10. Machine
Learning
Flattening
0
1
2
8
9
7
Deep Learning
Cluster
analysis
Supervised
analysis

11. Scientific
Software

12. Scientific
Software
g:Profiler
biit.cs.ut.ee/gprofiler/
biit.cs.ut.ee/mem/
MEM

13. Scientific
Software
funcExplorer
biit.cs.ut.ee/funcexplorer
PAWER
biit.cs.ut.ee/pawer
ClustVis
biit.cs.ut.ee/clustvis
g:Profiler
biit.cs.ut.ee/gprofiler/
biit.cs.ut.ee/mem/
MEM

14. Scientific
Software
funcExplorer
biit.cs.ut.ee/funcexplorer
PAWER
biit.cs.ut.ee/pawer
ClustVis
biit.cs.ut.ee/clustvis
g:Profiler
biit.cs.ut.ee/gprofiler/
biit.cs.ut.ee/mem/
MEM
~137 000 unique users a year

15. Biological
Imaging

16. Biological
Imaging
1
2
3
4 5
6
7
Microscopy imaging

17. Biological
Imaging
1
2
3
4 5
6
7
Microscopy imaging
Retinopathy
detection
Classification of
skin cancer

18. OMICS

19. OMICS
Genomics (DNA)
TACGGTATCAA ATCG
TA
A
TGCCATAT TGTAGC
T T TGT
A
G
CAA
AT
C
A
T…
T…
Gene

20. OMICS
Genomics (DNA)
TACGGTATCAA ATCG
TA
A
TGCCATAT TGTAGC
T T TGT
A
G
CAA
AT
C
A
T…
T…
Gene
G
UAU CAA A
G UAU
G
CAUAUUGU
A
GUAUA
Transcriptomics (RNA)

21. OMICS
Genomics (DNA)
TACGGTATCAA ATCG
TA
A
TGCCATAT TGTAGC
T T TGT
A
G
CAA
AT
C
A
T…
T…
Gene
G
UAU CAA A
G UAU
G
CAUAUUGU
A
GUAUA
Transcriptomics (RNA)
Ribosome

22. OMICS
Genomics (DNA)
TACGGTATCAA ATCG
TA
A
TGCCATAT TGTAGC
T T TGT
A
G
CAA
AT
C
A
T…
T…
Gene
G
UAU CAA A
G UAU
G
CAUAUUGU
A
GUAUA
Transcriptomics (RNA)
Ribosome
Amino Acids

23. OMICS
Genomics (DNA)
TACGGTATCAA ATCG
TA
A
TGCCATAT TGTAGC
T T TGT
A
G
CAA
AT
C
A
T…
T…
Gene
G
UAU CAA A
G UAU
G
CAUAUUGU
A
GUAUA
Transcriptomics (RNA)
Ribosome
Amino Acids
Proteomics (Proteins)

24. OMICS
Genomics (DNA)
TACGGTATCAA ATCG
TA
A
TGCCATAT TGTAGC
T T TGT
A
G
CAA
AT
C
A
T…
T…
Gene
G
UAU CAA A
G UAU
G
CAUAUUGU
A
GUAUA
Transcriptomics (RNA)
Ribosome
Amino Acids
Proteomics (Proteins)Metabolomics
(Metabolites)

25. Personalised
Medicine

26. Personalised
Medicine
Genetic
background
?

27. Personalised
Medicine
?John, 32, Male
Dr. Peterson Logout
related medical reports
2010 2011 2011
09 10 11 12 01 02 03 04
normal
Sugar
Electronic health records
Genetic
background

28. Pharmacogenomics Personalised
Medicine
?John, 32, Male
Dr. Peterson Logout
related medical reports
2010 2011 2011
09 10 11 12 01 02 03 04
normal
Sugar
Electronic health records
Genetic
background

29. Data
Management

30. Data
Management
Creating infrastructure
for life sciences
https://elixir.ut.ee/

31. Data
Management
Creating infrastructure
for life sciences
High Performance
Computing Centre
https://hpc.ut.ee/
https://elixir.ut.ee/

32. Teaching &
Training

33. Teaching &
Training
Teaching &
Training

34. Scientific
Software
Statistical Analysis
Biological
Imaging
OMICS
Teaching &
Training
Personalised
Medicine
Data
management
Machine
Learning

35. https://biit.cs.ut.ee/
Thank you!