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We present a system for miRNA classification that implements a wide variety of miRNA features found in literature: structural, thermodynamical, information-theoretical, statistical, and comparative. A total of 1485 features are computed and various tests are performed. The classifier of choice used is Random Forests, which is also employed along with various feature selection strategies to determine...
MicroRNAs (miRNAs) have been found in diverse organisms and play critical role in gene expression regulations of many essential cellular processes. Discovery of miRNAs and identification of their target genes are fundamental to the study of such regulatory circuits. To distinguish the real pre-miRNA from other stem loop hairpins with similar stem loop (pseudo pre-miRNA) is an important task in molecular...
Feature selection is a process to select a subset of original features. It can improve the efficiency and accuracy by removing redundant and irrelevant terms. Feature selection is commonly used in machine learning, and has been wildly applied in many fields. we propose a new feature selection method. This is an integrative hybrid method. It first uses Affinity Propagation and SVM sensitivity analysis...
This paper explores a sensor fusion method within Smart Homes to be used to monitor human activities in addition to managing uncertainty in sensor based readings. A case study has shown that the Dempster-Shafer theory of evidence can incorporate the uncertainty derived from the sensor errors and the sensor context and infer the activity. The results from this work show that this method can detect...
Between the information transfer rate and the classification accuracy of a brain computer interface (BCI) system a balance occurs. If we want higher correct classification rates the BCI system will consequently become slower. Otherwise, a faster (online) BCI system assumes a lower classification rate. If we analyze the human motor system (HMS) we can view the hierarchical organization (with different...
Computer-assisted assessment of summary writings is a challenging area which has recently attracted much interest from the research community. This is mainly due to the advances in other areas such as information extraction and natural language processing which have made automatic summary assessment possible. Different techniques such as Latent Semantic Analysis, n-gram co-occurrence and BLEU have...
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