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- 2Machine Learning
- 2Proteome Analyst
- 1Arabidopsis-thaliana
- 1Bayesian belief network
- 1Berries
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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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Fatty acid composition of developing sea buckthorn (Hippophae rhamnoides L.) berry and the transcriptome of the mature seed
Download2012
Snyder, Crystal L., Schroeder, William R., Wishart, David, Cram, Dustin, Fatima, Tahira, Datla, Raju, Weselake, Randall J., Krisha, Priti
Background Sea buckthorn (Hippophae rhamnoides L.) is a hardy, fruit-producing plant known historically for its medicinal and nutraceutical properties. The most recognized product of sea buckthorn is its fruit oil, composed of seed oil that is rich in essential fatty acids, linoleic (18∶2ω-6) and...
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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...
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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...
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Quantifying the Uncertainty of a Belief Net Response: Bayesian Error-Bars for Belief Net Inference
Download2007
Van Allen, Tim, Greiner, Russell, Singh, Ajit, Hooper, Peter
Technical report TR07-11. A Bayesian belief network models a joint distribution over variables using a DAG to represent variable dependencies and network parameters to represent the conditional probability of each variable given an assignment to its immediate parents. Existing algorithms assume...
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2005
Greiner, Russell, Schmidt, Mark, Levner, Ilya
Technical report TR05-19. Detecting and segmenting brain tumors in Magnetic Resonance Images (MRI) is an important but time-consuming task performed by medical experts. Automating this process is a challenging task due to the often high degree of intensity and textural similarity between normal...