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ISBRA
2009
Springer
16 years 1 days ago
Using Gene Expression Modeling to Determine Biological Relevance of Putative Regulatory Networks
Identifying gene regulatory networks from high-throughput gene expression data is one of the most important goals of bioinformatics, but it remains difficult to define what makes a...
Peter Larsen, Yang Dai
PRIB
2009
Springer
209views Bioinformatics» more  PRIB 2009»
15 years 11 months ago
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
For many biomedical modelling tasks a number of different types of data may influence predictions made by the model. An established approach to pursuing supervised learning with ...
Yiming Ying, Colin Campbell, Theodoros Damoulas, M...
BIBM
2007
IEEE
15 years 11 months ago
A Semi-supervised Learning Approach to Disease Gene Prediction
Discovering human disease-causing genes (disease genes in short) is one of the most challenging problems in bioinformatics and biomedicine, as most diseases are related in some wa...
Thanh Phuong Nguyen, Tu Bao Ho
ISBRA
2007
Springer
15 years 11 months ago
Discovering Relations Among GO-Annotated Clusters by Graph Kernel Methods
The biological interpretation of large-scale gene expression data is one of the challenges in current bioinformatics. The state-of-theart approach is to perform clustering and then...
Italo Zoppis, Daniele Merico, Marco Antoniotti, Bu...
ISBRA
2007
Springer
15 years 11 months ago
Clustering Algorithms Optimizer: A Framework for Large Datasets
Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are r...
Roy Varshavsky, David Horn, Michal Linial