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- W17419921 abstract "Genes and gene products regulate all of the processes in the cell, as they react to developmental or environmental events. Active (expressed) genes regulate the activity (expression) of other genes by coding for proteins that physically interact with their DNA. In that sense, all the genes in an organism are parts of a gene regulatory network that describes their activities and relationships. The therapeutic benefits of having a blueprint of a gene network are enormous because with it, the organism's responses can then be understood and even modified. The availability of different types of genomic and other large-scale biological data, most notably microarray gene expression data, make it possible to infer gene regulatory relationships and even reason about genetic networks. In this dissertation we present efficient algorithms and implementations for gene regulation inference through analysis and integration of microarray and other types of large-scale biological data. In particular, we describe one of the first software systems for gene network inference from expression data, based on combinatorial graph optimization, which yields biologically interesting gene networks of yeast. We also develop methods for analysis of time-series microarray data, used to interleave yeast genes' expression across experiments. Finally, we design and implement a system for general integrated analysis of heterogeneous data, based on consensus clustering. It works very well in practice, offering useful integrations of microarray with other large-scale experiments." @default.
- W17419921 created "2016-06-24" @default.
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- W17419921 date "2002-01-01" @default.
- W17419921 modified "2023-09-24" @default.
- W17419921 title "Computational inference of gene regulation" @default.
- W17419921 hasPublicationYear "2002" @default.
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