Studiegids

nl en

Biological data and Knowledge Integration

Vak
2014-2015

Description

E-Science can be defined as computationally intensive science, which is carried out by distributed networks of researchers, using distributed resources and often requiring the analysis of Big Data. In the post-genomic era, modern bioinformatics faces all of these challenges. There is a wealth of data available, but identifying, comparing and integrating disparate resources, in order to perform analyses, requires the use of larger computational infrastructures and e-science technologies.
This course investigates methods and techniques for analysing, manipulating and integrating bioinformatics data, with particular focus on omics data. We will discuss topics such as distributed bioinformatics resources, scientific workflows, ontologies, knowledge discovery, and data sharing.
The course is a combination of lectures, practical assignments, project work and seminar sessions. Through practical assignments and project work, students will gain hands-on experience, working on case studies involving the analysis and interpretation of omics data. Through seminar sessions, students will critically analyse and discuss current research papers in the field.
Participation in all sessions is mandatory.

Goals

  • Gain experience in data and knowledge integration methodologies
    • Understand the opportunities and problems caused by the exponential growth of biological data, with particular focus on omics data
    • Become acquainted with the use of e-Science techniques in bioinformatics research
    • Critically present and discuss academic papers

Prerequisites

Admissions requirements: Bachelor degree in Biology, Biomedical Science, LST, Pharmaceutical Science, or Completion of Computational Molecular Biology (4343COMOB).

Literature

Suggested reading and materials will be available on the course website after enrolment.

Table of Contents

  • Integrating distributed biological data sources
    • Working with omics data
    • Web Services and Workflows
    • Semantic web and Linked Data
    • Mining data and knowledge from the literature

Forms of Work

  • Lectures, Computer practicals, Project assignment, Seminar sessions with paper presentations
    Examination
    • Participation in seminar discussions and presentation of a journal paper (30%)
    • Practical exercises (10%)
    • Completion of a practical project and report (60%)

Contact information

Study coordinator Computer Science, Riet Derogee