Noah W. Smith

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We have developed GAMI, an approach to motif inference that uses a genetic algorithms search and is designed specifically to work with divergent species and possibly long nucleotide sequences. The system design reduces the size of the search space as compared to typical window-location approaches for motif inference. This paper describes the motivation and(More)
Cognitive models and intelligent agents are becoming more complex and pervasive. It is time again to consider high-level behavior representation languages and development environments that make it easier to create, share, and reuse cognitive models. One of these languages is Herbal, a high-level behavior representation language. Users represent knowledge in(More)
We propose a new method for collecting information on regulatory elements found by any motif discovery program. We suggest that combining the results of n leave-one-out motif discovery runs provides additional information. By examining motifs found in n - 1 of the sequences and scoring them on the remaining sequence, we overcome some of the issues arising(More)
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