Advanced information processing in bioinformatics

General info

Date from - to: 01 Sep 2005 - 01 Apr 2010
Project leader(s): Kok, Joost Prof. dr.
Participant(s): Breit, Timo Dr.; Siebes, Arno

Abstract

Data sets have increased dramatically in size and variety. Simultaneously, computational methods for extracting information from large quantities of data have been developed. In collaborations, information from different data streams comes together. This poses a challenge with regard to the integration of heterogeneous data. This project focuses on advanced information processing: algorithms, databases, and tools, that, when integrated in the collaborative information facilities of VL-e (Virtual Lab e-science), will provide a powerful bioinformatics environment. Semantic models within bioinformatics are needed, and advanced generic tools for data mining, and tools and methods for data interlinking and integration. Also, the design and development of a pattern-based framework to support the knowledge discovery efforts of bioinformatics researchers is investigated.

Link to the end report of this project

Publications

Clinical subtypes of Parkinson's disease The identification of Parkinson's disease subtypes using cluster analysis: a systematic review
Review of designing interactions
Screen of MicroRNA targets in zebrafish using heterogeneous data sources; a case study for dre-miR-10 and dre-miR-196
Improving a local learning technique for queue wait time predictions
Preserving privacy through data generation
An R Package for Subtype Discovery Examplified on Chemoinformatics Data
StreamKrimp: Detecting Change in Data Streams
A semantic web approach applied to integrative bioinformatics experimentation: a biological use case with genomics data
A comparison of two methods for finding groups using heat maps and model based clustering
Information visualization from ontology
miRNA Target Prediction through Mining of miRNA Relationships
Chacterising the difference
On The Design of Knowledge Discovery Services: Design Patterns and Their Application In A Use Case Implementation
Towards Service-Oriented Knowledge Discovery: A Case Study
Does SVM Really Scale Up to Large Bag of Words Feature Spaces?
Stability of Clusters for Different Time Adjustments in Complex Disease Research
A Scenario Implementation in R for Subtype Discovery Examplified on Chemoinformatics Data
Comparison of SVM and some Older Classification algorithms in Text Classification Tasks