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2003
Greiner, Russ, Poulin, B., Lu, Paul, Anvik, J., Lu, Z., Macdonell, Cam, Wishart, David, Eisner, Roman, Szafron, Duane
Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a...
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2001
Schaeffer, Jonathan, Charter, K., Lu, Paul, Szafron, Duane, Parsons, I., Driga, A.
Technical report TR01-10. For two DNA or protein sequences of length m and n, dynamic programming alignment algorithms like Needleman-Wunsch and Smith-Waterman take O(m x n) time and use O(m x n) space, so we refer to them as full matrix (FM) algorithms. This space requirement means that large...
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2003
Greiner, Russell, Wishart, David, Eisner, Roman, Lu, Z., Lu, Paul, Macdonell, Cam, Poulin, B., Szafron, Duane, Anvik, J.
Technical report TR03-14. Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy...