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BioInformatics and
Information Technology
research group
https://biit.cs.ut.ee/
Statistical Analysis
Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?
Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?
Hypothe...
Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?
1
2345
...
Statistical Analysis
Power calculations
Would it be enough to
collect N samples to reliably
detect the difference?
1
2345
...
Machine
Learning
Machine
Learning
Cluster
analysis
Supervised
analysis
Machine
Learning
Flattening
0
1
2
8
9
7
Deep Learning
Cluster
analysis
Supervised
analysis
Scientific
Software
Scientific
Software
g:Profiler
biit.cs.ut.ee/gprofiler/
biit.cs.ut.ee/mem/
MEM
Scientific
Software
funcExplorer
biit.cs.ut.ee/funcexplorer
PAWER
biit.cs.ut.ee/pawer
ClustVis
biit.cs.ut.ee/clustvis
g:Pro...
Scientific
Software
funcExplorer
biit.cs.ut.ee/funcexplorer
PAWER
biit.cs.ut.ee/pawer
ClustVis
biit.cs.ut.ee/clustvis
g:Pro...
Biological
Imaging
Biological
Imaging
1
2
3
4 5
6
7
Microscopy imaging
Biological
Imaging
1
2
3
4 5
6
7
Microscopy imaging
Retinopathy
detection
Classification of
skin cancer
OMICS
OMICS
Genomics (DNA)
TACGGTATCAA ATCG
TA
A
TGCCATAT TGTAGC
T T TGT
A
G
CAA
AT
C
A
T…
T…
Gene
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
...
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
...
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
...
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
...
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
...
Personalised
Medicine
Personalised
Medicine
Genetic
background
?
Personalised
Medicine
?John, 32, Male
Dr. Peterson Logout
related medical reports
2010 2011 2011
09 10 11 12 01 02 03 04
n...
Pharmacogenomics Personalised
Medicine
?John, 32, Male
Dr. Peterson Logout
related medical reports
2010 2011 2011
09 10 11...
Data
Management
Data
Management
Creating infrastructure
for life sciences
https://elixir.ut.ee/
Data
Management
Creating infrastructure
for life sciences
High Performance
Computing Centre
https://hpc.ut.ee/
https://eli...
Teaching &
Training
Teaching &
Training
Teaching &
Training
Scientific
Software
Statistical Analysis
Biological
Imaging
OMICS
Teaching &
Training
Personalised
Medicine
Data
management...
https://biit.cs.ut.ee/
Thank you!
Biit group 2018
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Biit group 2018

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Presentation about Bioinformatics and Information Technology research group from the Institute of Computer Science at University of Tartu (https://biit.cs.ut.ee/)

Publié dans : Sciences
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Biit group 2018

  1. 1. BioInformatics and Information Technology research group https://biit.cs.ut.ee/
  2. 2. Statistical Analysis
  3. 3. Statistical Analysis Power calculations Would it be enough to collect N samples to reliably detect the difference?
  4. 4. Statistical Analysis Power calculations Would it be enough to collect N samples to reliably detect the difference? Hypothesis testing p < 0.05
  5. 5. 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
  6. 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 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
  7. 7. Machine Learning
  8. 8. Machine Learning Cluster analysis Supervised analysis
  9. 9. Machine Learning Flattening 0 1 2 8 9 7 Deep Learning Cluster analysis Supervised analysis
  10. 10. Scientific Software
  11. 11. Scientific Software g:Profiler biit.cs.ut.ee/gprofiler/ biit.cs.ut.ee/mem/ MEM
  12. 12. 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
  13. 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 ~137 000 unique users a year
  14. 14. Biological Imaging
  15. 15. Biological Imaging 1 2 3 4 5 6 7 Microscopy imaging
  16. 16. Biological Imaging 1 2 3 4 5 6 7 Microscopy imaging Retinopathy detection Classification of skin cancer
  17. 17. OMICS
  18. 18. OMICS Genomics (DNA) TACGGTATCAA ATCG TA A TGCCATAT TGTAGC T T TGT A G CAA AT C A T… T… Gene
  19. 19. 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)
  20. 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) Ribosome
  21. 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 Amino Acids
  22. 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 Proteomics (Proteins)
  23. 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)Metabolomics (Metabolites)
  24. 24. Personalised Medicine
  25. 25. Personalised Medicine Genetic background ?
  26. 26. 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
  27. 27. 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
  28. 28. Data Management
  29. 29. Data Management Creating infrastructure for life sciences https://elixir.ut.ee/
  30. 30. Data Management Creating infrastructure for life sciences High Performance Computing Centre https://hpc.ut.ee/ https://elixir.ut.ee/
  31. 31. Teaching & Training
  32. 32. Teaching & Training Teaching & Training
  33. 33. Scientific Software Statistical Analysis Biological Imaging OMICS Teaching & Training Personalised Medicine Data management Machine Learning
  34. 34. https://biit.cs.ut.ee/ Thank you!

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