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» A framework for modelling virus gene expression data
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ACL
2006
15 years 2 months ago
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
ICML
2007
IEEE
16 years 1 months ago
Two-view feature generation model for semi-supervised learning
We consider a setting for discriminative semisupervised learning where unlabeled data are used with a generative model to learn effective feature representations for discriminativ...
Rie Kubota Ando, Tong Zhang
BMCBI
2008
134views more  BMCBI 2008»
15 years 25 days ago
Identification of transcription factor contexts in literature using machine learning approaches
Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While man...
Hui Yang, Goran Nenadic, John A. Keane
BMCBI
2010
118views more  BMCBI 2010»
15 years 26 days ago
Identifying differentially regulated subnetworks from phosphoproteomic data
Background: Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrati...
Martin Klammer, Klaus Godl, Andreas Tebbe, Christo...
97
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BMCBI
2010
105views more  BMCBI 2010»
15 years 26 days ago
Effects of scanning sensitivity and multiple scan algorithms on microarray data quality
Background: Maximizing the utility of DNA microarray data requires optimization of data acquisition through selection of an appropriate scanner setting. To increase the amount of ...
Andrew Williams, Errol M. Thomson