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» Gene function prediction using labeled and unlabeled data
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NIPS
2003
14 years 11 months ago
Semi-Supervised Learning with Trees
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure ...
Charles Kemp, Thomas L. Griffiths, Sean Stromsten,...
BMCBI
2007
157views more  BMCBI 2007»
14 years 10 months ago
Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory
Background: Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many i...
Feng Luo, Yunfeng Yang, Jianxin Zhong, Haichun Gao...
ICML
2003
IEEE
15 years 11 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
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BMCBI
2007
163views more  BMCBI 2007»
14 years 10 months ago
Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA - a Bayesian approac
Background: Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge. Resul...
Carmen Pin, Mark Reuter
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
15 years 4 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...