Search
Skip to Search Results- 2Bioinformatics
- 2Machine Learning
- 2Proteome Analyst
- 1BN classifiers
- 1Bayesian belief network
- 1Explain
-
2004
Technical report TR04-22. Model selection problem in Bayesian belief network (BN) structure learning is a classicial problem in the BN literature. To do model selection in BN structure learning, we need a evaluation score and a searching procedure. The generative criteria, AIC, BIC and BDe, are...
-
Proteome Analyst - Transparent High-throughput Protein Annotation: Function, Localization and Custom Predictors
Download2003
Lu, Z., Eisner, Roman, Lu, Paul, Macdonell, Cam, Szafron, Duane, Greiner, Russell, Poulin, B., Wishart, David, Anvik, J., Habibi-Nazhad, B.
Technical report TR03-05. Modern sequencing technology now permits the sequencing of entire genomes, leading to thousands of new gene sequences in need of detailed annotation. It is too time consuming to predict the properties of each protein sequence manually and to organize the results of many...
-
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...
-
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...