Data Mining & Integration with Networks

bio-it   2015-12-09   Comments Off on Data Mining & Integration with Networks

Date/Time
Date(s) - 2015-12-09 - 2015-12-11
09:00 CET - 13:00 CET

Location
ATC Computer Training Lab

Categories


Aim:
This intermediate-level course will demonstrate through a combination of lectures and hands-on tutorials how to perform integration of heterogeneous biological data.
Integrative analytics is currently one of the hottest fields in bio-medical data research and paradoxically adding supporting information helps to reduce data complexity, and to infer a more complete picture of condition-dependent biological responses.

Requirements for participation:

  • basic R – the course will be predominantly R-based, familiarity with other programing languages will suffice
  • basic Cytoscape – for this course you only need to know how the CS interface is organized, contact us for a basic tutorial, which you can work through before the course

Target audience:

  • Experimentalists with basic computational skills (own experience, or EMBL courses: Introduction to R / Python / Linux Command line, Introduction to Network Biology)
  • Computationally-focused scientists (maths, bioinformatics, biology etc) with interest in learning about network-based integrative approaches

Day 1

  • Introduction to concepts of Graph Theory

Practical:

  • Mining public resources
  • Network inference from data, adjacency matrices, and combining data on graphs
  • Enrichment and functional integration of -omics data

Day 2

  • Using Kernels on graph nodes to compute similarities, and integrate heterogenous biological data
  • Mining dynamic networks: third order tensors for representation of dynamic graphs, and tensor factorization for characterizing time-evolving patterns

Practical:

  • computing various kernels from different data sets
  • evaluating kernels on information retrieval tasks
  • using kernels to make predictions

Day 3

  • Introduction to network community structure and clustering algorithms

Practical:

  • Methods to detect, evaluate, and visualize community structure in complex integrated biological networks

Tutors:Matt Rogon (EMBL Centre for Network Analysis), Jean-Karim Hériché (Ellenberg Group, CBB), and Aaron Brooks (Steinmetz Group, GB)

This is an internal course free to all EMBL scientists, as well as associated students.