Machine Learning
Machine Learning - Special issue on learning with probabilistic representations
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Text classification from positive and unlabeled documents
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Positive Sample Only Learning (PSOL) for Predicting RNA Genes in E. coli
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Splice site identification by idlBNs
Bioinformatics
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning Bayesian classifiers from positive and unlabeled examples
Pattern Recognition Letters
Feature subset selection from positive and unlabelled examples
Pattern Recognition Letters
A novel computational method for predicting disease genes based on functional similarity
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
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The discovery of the genes involved in genetic diseases is a very important step towards the understanding of the nature of these diseases. In-lab identification is a difficult, time-consuming task, where computational methods can be very useful. In silico identification algorithms can be used as a guide in future studies. Previous works in this topic have not taken into account that no reliable sets of negative examples are available, as it is not possible to ensure that a given gene is not related to any genetic disease. In this paper, this feature of the nature of the problem is considered, and identification is approached as a partially supervised classification problem. In addition, we have performed a more specific method to identify disease genes by classifying, for the first time, genes causing dominant and recessive diseases independently. We base this separation on previous results that show that these two types of genes present differences in their sequence properties. In this paper, we have applied a new model averaging algorithm to the identification of human genes associated with both dominant and recessive Mendelian diseases.