ERCIM News 104
January 2016
Special theme Tackling Big Data in the Life Sciences
Guest editors Roeland Merks (CWI) and Marie-France Sagot (Inria)
Tackling Big Data in the Life Sciences - Introduction to the Special Theme
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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"
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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
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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
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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.
- Eliminating Blind Spots in Genetic Variant Discovery
- Computational Estimation of Chromosome Structure
- Modelling Approaches to Inform the Control and Management of Invasive Seaweeds
- Kbdock – Searching and Organising the Structural Space of Protein-Protein Interactions
- The Source of the Data Flood: Sequencing Technologies
- Big Data in Support of the Digital Cancer Patient
- Towards an On-board Personal Data Mining Framework For P4 Medicine
- Can Data-driven Self-Management Reduce Low Back Pain?
- Twitter can Help to Find Adverse Drug Reactions
- Trust for the “Doctor in the Loop”
- Big Data Takes on Prostate Cancer
- Mining Electronic Health Records to Validate Knowledge in Pharmacogenomics
- Modelling the Growth of Blood Vessels in Health and Disease
- Modelling? Using Standards Can Help You
- Understanding Metadata to Exploit Life Sciences Open Data Datasets
- Management of Big and Open Data in the Life Cycle Assessment of Ecosystem Services
- WITDOM: Empowering Privacy and Security in Non-trusted Environments
- Women in Informatics Research and Education
- Reflections from a Leadership Program for Women in Scientific Positions
- The Girl Project Ada: A Successful Project for Recruiting Female Students to the ICT-Studies at NTNU
- Inspire, Network, Encourage – womENcourage 2015
- Women in IT Security Research: The Case of SBA Research
- Integrating Gender in Research Institutions in Greece
- Secure and Privacy-Preserving Identity Management in the Cloud
- PRISMACLOUD – Privacy and Security Maintaining Services in the Cloud
- TREDISEC: Towards Realizing a Truly Secure and Trustworthy Cloud
- Smart Devices for the Home of the Future: A New Model for Collaboration
- FOODWEB – Studying Food Consumption and Production Patterns on the Web
- Robots Learn Actions and Cooperative Tasks by Imitation
- Unlocking the Secrets of the Dynamics of Earth’s Interior
- International Workshop on Computational Intelligence for Multimedia Understanding
- Activities of the ERCIM Working Group “Dependable Embedded Software-Intensive Systems”
- Lynda Hardman President Informatics Europe
- More on IFIP TC6’s Open Digital Library
- QuSoft Research Center for Quantum Software Launched
- Online Social Networks: Human Cognitive Constraints in Facebook and Twitter Personal Graphs