Please note: This instance is for testing/development, and any content submitted may be changed or deleted without warning.
Training eSupport System
  • Log In
    • Login
    • Register
  • About
  • Events
  • Materials
  • e-Learning
  • Workflows
  • Collections
  • Learning paths
  • Directory
    • Trainers
    • Providers
    • Nodes

TeSS makes use of some necessary cookies to provide its core functionality.

See our Privacy Policy for more information.

You can modify your cookie preferences at any time here, or from the link in the footer.

Allow necessary cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Scientific topic
    • Probability
    • Antimicrobial stewardship1
    • Assembly1
    • Bayesian methods1
    • Biological sequences1
    • Biostatistics1
    • DNA polymorphism1
    • DNA variation1
    • Descriptive statistics1
    • Exomes1
    • Gaussian processes1
    • Genetic variation1
    • Genome annotation1
    • Genomes1
    • Genomic variation1
    • Genomics1
    • Inferential statistics1
    • Markov processes1
    • Medical microbiology1
    • Microbial genetics1
    • Microbial physiology1
    • Microbial surveillance1
    • Microbiological surveillance1
    • Microbiology1
    • Microsatellites1
    • Molecular infection biology1
    • Molecular microbiology1
    • Multivariate statistics1
    • Mutation1
    • Personal genomics1
    • Polymorphism1
    • Probabilistic graphical model1
    • RFLP1
    • SNP1
    • Sequence analysis1
    • Sequence assembly1
    • Sequence databases1
    • Single nucleotide polymorphism1
    • Somatic mutations1
    • Statistics1
    • Statistics and probability1
    • Synthetic genomics1
    • VNTR1
    • Variable number of tandem repeat polymorphism1
    • Viral genomics1
    • Whole genomes1
    • snps1
    • Show N_FILTERS more
    • Content provider
    • Galaxy Training1
    • Show N_FILTERS more
    • Keyword
    • Machine learning1
    • Pan-cancer1
    • Statistics and machine learning1
    • cancer biomarkers1
    • oncogenes and tumor suppressor genes1
    • Show N_FILTERS more
    • Difficulty level
    • Beginner1
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International1
    • Show N_FILTERS more
    • Target audience
    • Students1
    • Show N_FILTERS more
    • Author
    • Daniel Blankenberg1
    • Vijay1
    • Show N_FILTERS more
    • Contributor
    • Vijay
    • Saskia Hiltemann19
    • Björn Grüning14
    • Helena Rasche14
    • Martin Čech12
    • Anup Kumar11
    • Armin Dadras9
    • Kaivan Kamali7
    • Teresa Müller6
    • Alireza Khanteymoori5
    • Bérénice Batut5
    • Cristóbal Gallardo2
    • Fabio Cumbo2
    • Gildas Le Corguillé2
    • Simon Bray2
    • qiagu2
    • Bert Droesbeke1
    • Daniel Sobral1
    • Daniela Schneider1
    • Enis Afgan1
    • Michelle Terese Savage1
    • Mélanie Petera1
    • Nate Coraor1
    • Niall Beard1
    • Nicola Soranzo1
    • dlal-group1
    • Show N_FILTERS more
    • Resource type
    • e-learning1
    • Show N_FILTERS more
    • Related resource
    • Associated Workflows
    • Associated Training Datasets1
    • Show N_FILTERS more
  • Show disabled materials
  • Show archived materials
    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month

Training materials

  • Subscribe via email

Email Subscription

Register training material

Scientific topics: Probability

and Contributors: Vijay

and Related resources: Associated Workflows

1 material found
  • e-learning

    PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis

    • beginner
    Statistics and probability Machine learning Pan-cancer Statistics and machine learning cancer biomarkers oncogenes and tumor suppressor genes
Training eSupport System
contact@example.com
Contribute
About TeSS
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.0
Source code
API documentation
Bioschemas testing tool

TeSS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676559.