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Biit group 2018
The document outlines the research activities of a bioinformatics and information technology group, focusing on statistical analysis, hypothesis testing, and various omics studies. It highlights the application of machine learning and deep learning techniques for data analysis, as well as developments in scientific software and personalized medicine. Additionally, it mentions the importance of data management and high-performance computing in supporting life sciences research.
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Biit group 2018
- 1. BioInformatics and Information Technology researchgroup https://biit.cs.ut.ee/
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- 4. Statistical Analysis Power calculations Wouldit be enough to collect N samples to reliably detect the difference?
- 5. Statistical Analysis Power calculations Wouldit be enough to collect N samples to reliably detect the difference? Hypothesis testing p < 0.05
- 6. Statistical Analysis Power calculations Wouldit 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 Wouldit 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
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- 19. OMICS Genomics (DNA) TACGGTATCAA ATCG TA A TGCCATATTGTAGC T T TGT A G CAA AT C A T… T… Gene
- 20. OMICS Genomics (DNA) TACGGTATCAA ATCG TA A TGCCATATTGTAGC 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 TGCCATATTGTAGC 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 TGCCATATTGTAGC 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 TGCCATATTGTAGC 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 TGCCATATTGTAGC 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)
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- 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
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- 30. Data Management Creating infrastructure for lifesciences https://elixir.ut.ee/
- 31. Data Management Creating infrastructure for lifesciences High Performance Computing Centre https://hpc.ut.ee/ https://elixir.ut.ee/
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