Multi-scale analysis of high-throughput ‘omics’ data

General info

Date from - to

01 Jan 2009

Project leader(s)

Reinders, Marcel Prof. dr. ir.

Abstract

This project aims to exploit scale-space theory for the analysis of high-throughput genomics data to delineate the complex interplay among biological elements of molecular processes that takes place in living systems.

The notion of scale plays an important role in how we observe our environment. For instance, considering a tree from a distance of a light-year or a nanometer is meaningless, but would make sense at a distance of a few meters. Clearly, objects only exist as meaningful entities across a certain range of scales. In the field of machine vision, it has been argued that the only appropriate approach to this problem is to analyze the data at all scales simultaneously. This resulted in the scale-space theory, which gives a framework for multi-scale representation of images or other signals.

We will explore scale-space representations of various sources of genomics data as well as investigate how we can employ these representations to benefit follow-up analyses such as classification or interaction inference. The challenge will be to develop innovative methodologies that can exploit available genomics data at multiple scales simultaneously, rather than at a single scale and to explore possible applications of these methods using datasets generated at the Kluyver Centre for Genomics of Industrial Fermentation, the Netherlands Cancer Institute (NKI) and publicly available datasets.