This book presents recent methods for Systems Genetics (SG) data
analysis, applying them to a suite of simulated SG benchmark
datasets. Each of the chapter authors received the same datasets to
evaluate the performance of their method to better understand which
algorithms are most useful for obtaining reliable models from SG
datasets. The knowledge gained from this benchmarking study will
ultimately allow these algorithms to be used with confidence for SG
studies e.g. of complex human diseases or food crop improvement.
The book is primarily intended for researchers with a background in
the life sciences, not for computer scientists or
statisticians.
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