Najnowsze osiągnięcia w bioinformatyce

Prowadzący: Joanna Szyda

Wykłady

  1. 05.10: Wykład organizacyjny
  2. 05.10: Wykład 2
    Stability of methods for differential expression analysis of RNA-seq data
  3. 11.10: Wykład 3 – kontynuacja poprzedniego zagadnienia
  4. 11.10: Wykład 4
    Interactions between the gut microbiome and host gene regulation in cystic fibrosis and Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)
  5. 18.10: Wykład 5 – kontynuacja poprzedniego zagadnienia
  6. 18.10: Wykład 6
    Clipper: p-value-free FDR control on high-throughput data from two conditions

Ćwiczenia:

  1. 07.10: The UK Biobank Scientific Conference 2020, D. Wilson – Genome-wide association studies of Covid19, minuty ~ 3:50-19:50
  2. dodatkowe publikacje dla tego tematu: tutaj i tutaj

  3. 14.10: The UK Biobank Scientific Conference 2020, G. Abecasis – Exome sequencing of UK Biobank Participants, minuty ~ 20:15-38:15
  4. 21.10: The UK Biobank Scientific Conference 2020, M. Loose – Whole genome sequencing, long and short reads, minuty ~ 38:25-50:25
  5. 28.10: The UK Biobank Scientific Conference 2021, C. Lindgren – Building maps for translation into Mechanisms and Medicine, minuty ~ 3:10-22.10
  6. 04.11: The UK Biobank Scientific Conference 2021, C. Whelan – Proteomics at Population Scale, minuty ~ 22:30-35:30
  7. 18.11: The UK Biobank Scientific Conference 2021, N. Samani – Telomeres in UK Biobank, minuty ~ 36:10-52:10
  8. 25.11: The UK Biobank Scientific Conference 2021, K. Beyli – Genetics mechanisms of COVID19, minuty ~ 10:30-24:30
  9. 02.12: Machine Learning for Computational Biology 2020, E. Segal – Personalized medicine based on microbiome and clinical data, minuty ~ 0:17:45 – 0:59:45 part 1
  10. 09.12: Machine Learning for Computational Biology 2020, E. Segal – Personalized medicine based on microbiome and clinical data, minuty ~ 0:17:45 – 0:59:45 part 2
  11. 16.12: Machine Learning for Computational Biology 2020, M. Saberian – Drug repurposing efficacy estimation on morphological analysis of SARS-COV-2 infected cells within a multiple instance learning framework, minuty ~ 1:43:20-1:59:20
  12. 22.12: Machine Learning for Computational Biology 2020, E. Gabbassov – Split strains, a tool to identify and separate mixed Micobacterium tuberculosis infections from WGS data, minuty ~ 2:46:00-3:05:00
  13. 13.01: Machine Learning for Computational Biology 2020, E. Cofer – AMBIENT: Accelerated convolutional Neural Network Architecture search for regulatory genomics, minuty ~ 3:06:10-3:24:00
  14. 20.01: Julia – intro
    self watching in advance:
    Is this course for me? Julia Programming For Nervous Beginners (Week 1 Lesson 0)
    watching (and trying) together:
    Your First Julia Code! Julia Programming For Nervous Beginners (Week 1 Lesson 1),
    Effects.jl: Effectively Understand Effects in Regression Models Phillip Alday | JuliaCon2021
  15. 27.01: Julia – intro
    self watching in advance:
    Deconstructing Your First Julia Code | Julia Programming For Nervous Beginners (Week 1 Lesson 2)
    watching (and trying) together:
    Debugging and Error Messages | Julia Programming For Nervous Beginners (Week 1 Lesson 3),
    PRS.jl: Fast Polygenic Risk Scores | Annika Faucon | JuliaCon2021