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...
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...
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...
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...
Clustering algorithms are employed in many bioinformatics tasks, including categorization of protein sequences and analysis of gene-expression data. Although these algorithms are r...