| Global analysis of large-scale chemical and biological experiments | |||||||||||
Abstract | |||||||||||
| Research in the life sciences is increasingly dominated by high-throughput data collection methods that benefit from a global approach to data analysis. Recent innovations that facilitate such comprehensive analyses are highlighted. Several developments enable the study of the relationships between newly derived experimental information, such as biological activity in chemical screens or gene expression studies, and prior information, such as physical descriptors for small molecules or functional annotation for genes. The way in which global analyses can be applied to both chemical screens and transcription profiling experiments using a set of common machine learning tools is discussed. | |||||||||||
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