ERCIM News 104

Tackling Big Data in the Life Sciences - Introduction to the Special Theme

special theme

by the guest editors Roeland Merks and Marie-France Sagot

The Life Sciences are traditionally a descriptive science, in which both data collection and data analysis both play a central role. The latest decennia have seen major technical advances, which have made it possible to collect biological data at an unprecedented scale. Even more than the speed at which new data are acquired, the very complexity of what they represent makes it particularly difficult to make sense of them. Ultimately, biological data science should further the understanding of biological mechanisms and yield useful predictions, to improve individual health care or public health or to predict useful environmental interferences.

“Because it’s 2016” - Introduction to the section "Women in ICT Research and Education"

Lynda Hardman

by the guest editor Lynda Hardman

The informatics and mathematics communities suffer from an affliction common to many technical and scientific fields, that fewer than 30% of those who choose to study and continue their profession in the field are women. This leads to a masculine-oriented culture that unwittingly discriminates against women.

Networks to the Rescue – From Big “Omics” Data to Targeted Hypotheses

by Gunnar Klau

CWI researchers are developing the Heinz family of algorithms to explore big life sciences data in the context of biological networks. Their methods recently pointed to a novel hypothesis about how viruses hijack signalling pathways.

Interactive Pay-As-You-Go-Integration of Life Science Data: The HUMIT Approach

by Christoph Quix, Thomas Berlage and Matthias Jarke

Biomedical research applies data-intensive methods for drug discovery, such as high-content analysis, in which a huge amount of substances are investigated in a completely automated way. The increasing amount of data generated by such methods poses a major challenge for the integration and detailed analysis of the data, since, in order to gain new insights, the data need to be linked to other datasets from previous studies, similar experiments, or external data sources. Owing to its heterogeneity and complexity, however, the integration of research data is a long and tedious task. The HUMIT project aims to develop an innovative methodology for the integration of life science data, which applies an interactive and incremental approach.