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MicroRNAs (miRNAs) are a class of small noncoding RNAs which have close relations with human diseases. Herein, predicting the novel associations between human diseases and miRNAS is urgently needed. However, only use of the experimental approaches to identity such relations have many choke points such as time-consumption and high cost. In this study, we adopt a network-based inference (NBI) based...
The structure of a protein is closely correlated to its function. Feature dimension reduction method is one of most famous machine learning tools. Some researchers have begun to explore feature dimension reduction method for computer vision problems. Few such attempts have been made for classification of high-dimensional protein data sets. In this paper, feature dimension reduction method is employed...
Assigning functions to proteins that have not been annotated is an important problem in the post-genomic era. Meanwhile, the availability of data on protein-protein interactions provides a new way to predict protein functions. Previously, several computational methods have been developed to solve this problem. Among them, Deng et al. developed a method based on the Markov random field (MRF). Lee et...
The membrane protein type is an important feature in characterizing the overall topological folding type of a protein or its domains therein. How to fast and efficiently annotate the type of an uncharacterized membrane protein is a challenge. Some discrete models, such as DC (dipeptide composition) have been proposed to represent a protein sequence in the field of predicting membrane protein types...
Using Multiple Kernels Learning(MKL) to integrate heterogeneous data sources to train Support Vector Machine(SVM) classifier is becoming popular. For the protein function prediction problem, all the function categories form a directed acyclic graph(DAG), that is, Gene Ontology(GO). Given a protein to be predicted, after applying a trained SVM to output probabilistic prediction at each function category...
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