Description:
The quantity and variety of molecular biological data presents a challenge for the field of machine learning. Converting this raw data into a unified model of the molecular machinery of the cell requires learning algorithms that are capable of incorporating significant prior biological knowledge and of deriving knowledge from multiple types of data. This talk describes two learning algorithms that address these core problems.
Speaker(s):
William Grundy, Columbia University
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