Development of generic integration methodology towards a life-sciences problem-solving environment by modelling of data and knowledge

General info

Date from - to

01 Apr 2005 - 01 Dec 2009

Project leader(s)

Breit, Timo Dr.

Participant(s)

Kok, Joost Prof. dr.

Roos, Marco Dr.

Abstract

The overall aim of this project is to develop problem-solving environments (PSEs) for computational life-sciences research. For this, semantically annotated data and biological knowledge was used in computational experiments to develop innovative methods, for example for data analysis or knowledge-discovery. Driven by a particular biological problem related to a test case, some analyses were performed using ad-hoc methods. This also involved research on the application of statistical, model-based, or data mining methods to specific problems. Furthermore, ad-hoc problem solving will provide requirements and raw building blocks for the development of the generic problem-solving environment. By using the results of the information analysis and the lessons learned from the problem driven approach, state-of-the-art methodology was developed and evaluated for the creation of dedicated life-sciences problem-solving environments. These included research on how ontologies could be used to provide a formal and non-ambiguous representation of data for robust and flexible data integration, as well as for the application of knowledge models for computational life-sciences research.

Link to the end report of this project

Publications

• A prototype knowledge base for the life sciences
• An ontology based approach for data integration; An application in Biomedical Research
• Data Lineage Model for Taverna Workflows with Lightweight Annotation Requirements
• Aualia Structures and their Impact on the Concrete Noun Categorization Task
• Multi-view Ontology Visualization
• Semantic Types of Some Generic Rlation Arguments: Detection and Evaluation
• A Local Alignment kernel in the Context of NLP
• Structuring mined knowledge for the support of hypothesis generation in molecular biology
• Calling on a million minds for communicty annotation in wikiproteins; why would key biology databases go wiki?
• Life Sciences on the semantic web: the nerocommons and beyond
• Salvaging Affymetrix probes after probe-level re-annotation
• SigWinR; the SigWin-detector updated and ported to R
• OligoRAP - an Oligo Re-Annotation Pipeline to improve annotation and estimate target specificity
• Advancing translational research with the Semantic Web
• De AIDA toolbox: Een gecombineerde aanpak voor het beheren van kennis
• On Emotion, Anticipation and adaption: Investigating the Potential of Affect-Controlled Selection of Anticipatory Simulation in Artificial Adaptive Agents
• Enable data transport between web services through alternative protocols and streaming
• The role of e-BioLabs in a life sciences collaborative working environment
• A survey of requirements for automated reasoning services for bio-ontologies in OWL
• Ontological Context Visualization
• Assignment of protein function and discovery of novel nucleolar proteins based on automatic analysis of MEDLINE
• Measuring concept relatedness using language models
• Parsimonious concept modeling
• Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification
• Screen of MicroRNA targets in zebrafish using heterogeneous data sources; a case study for dre-miR-10 and dre-miR-196
• An R Package for Subtype Discovery Examplified on Chemoinformatics Data
• Using R in Taverna: RShell v1.2
• SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences
• E-BioFlow: different perspectives on scientific workflows
• Information visualization from ontology