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<style face="normal" font="default" size="100%">Protein Interactions for Functional Genomics</style>

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<style face="normal" font="default" size="100%">Clustering - Class discovery in the post-genomic era</style>

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<style face="normal" font="default" size="100%">f single nucleotide polymorphism arrays: Design, tools and applications</style>

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<style face="normal" font="default" size="100%">Functional annotation of microarray experiments</style>

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<style face="normal" font="default" size="100%">New Trends in the Analysis of Functional Genomic Data</style>

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<style face="normal" font="default" size="100%">Data analysis and visualisation in genomics and proteomics</style>

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<style face="normal" font="default" size="100%">Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions</style>

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<style face="normal" font="default" size="100%">Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges</style>

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<style face="normal" font="default" size="100%">Ontologies and functional genomics</style>

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<style face="normal" font="default" size="100%">Salinibacter ruber: genomics and biogeography</style>

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<style face="normal" font="default" size="100%">Gene expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationship</style>

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<style face="normal" font="default" size="100%">Use of GO Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data</style>

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<style face="normal" font="default" size="100%">Using Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes</style>

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<style face="normal" font="default" size="100%">Microarray Data Processing And Analysis</style>

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<style face="normal" font="default" size="100%">Supervised Neural Networks For Clustering Conditions In DNA Array Data After Reducing Noise By Clustering Gene Expression Profiles</style>

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<style face="normal" font="default" size="100%">Using perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes</style>