![]() Some approaches to select negative examples have been proposed. Thus, an important step is to select a suitable set of negative examples from unlabeled examples before training classifier. In other words, for a functional class, we need to learn a classifier from positive and unlabeled examples. As a result, when training classifier for a functional class, we can only obtain labeled positive examples and many unlabeled ones. for a functional class, we only know which gene is assigned to it, but we are not sure that a gene has no this function except for too few genes. However, the available information from the annotation databases, such as GO, is only about positive examples, i.e. The mainstream approach is to transform it into a binary classification task for each functional class, which focuses on training a classifier such as SVM (support vector machine) with some labeled positive and negative examples. For an unknown gene, predicting its functions will assign some GO functional terms to it, which is called multi-label classification problem in machine learning community. GO functional annotation associates each gene or gene product to some functional terms. GO is a widely-used set of functional terms with which some genes are annotated, we also call functional terms as functional classes in related to classification problem from machine learning. Furthermore, many works have shown that integration of different kinds of data sources can considerably improve prediction results. These large data-sets have fueled an interest in computational approaches to gene function prediction, which promises to harness the information present in these large collections of data to automatically deduce accurate gene annotations. With the recent invention of several large-scale experimental methods, a wealth of functional genomic data was accumulated, including sequence, micro-array expression profile and protein-protein interaction data. One of the important challenges in the post-genome era is determining the functional role of all genes in the cell although about one-third of the genes have been annotated and deposited in database such GO(gene ontology). In addition, our method can also be used for other organisms such as human. The experiments showthat our approach has better generalized performance and practical prediction capacity. In test data and unknown genes data, we compute average and variant of measure F. On combined kernel ofYeast protein sequence, microarray expression, protein-protein interaction and GO functional annotation data, we compare SPE_RNE with other three typical methods in three classical performance measures recall R, precise P and their combination F: twoclass considers all unlabeled genes as negative examples, twoclassbal selects randomly same number negative examples from unlabeled gene, PSoL selects a negative examples set that are far from positive examples and far from each other. Lastly, an optimal SVM classifier are trained by using grid search technique. Secondly, representative negative examples are selected by training SVM(support vector machine) iteratively to move classification hyperplane to a appropriate place. Firstly, positive examples set is enlarged by creating synthetic positive examples. We propose a novel approach SPE_RNE to train classifier for each functional term. However, from various annotation database, we can only obtain few positive genes annotation for most offunctional terms, that is, there are only few positive examples for training classifier, which makes predicting directly gene function infeasible. Training binary classifier needs both positive examples and negative ones that have almost the same size. The prediction procedure is usually formulated as binary classification problem. A large amount of functional genomic data have provided enough knowledge in predicting gene function computationally, which uses known functional annotations and relationship between unknown genes and known ones to map unknown genes to GO functional terms.
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