Combining multi-species genomic data for microRNA identification using a Naı̈ve Bayes classifier
نویسندگان
چکیده
Motivation: Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naı̈ve Bayes classifier. It automatically generates a model from the training data, which consistsofsequenceandstructure informationofknownmiRNAsfroma
منابع مشابه
Combining Multi-Species Genomic Data for MicroRNA Identification Using a Naïve Bayes Classifier Machine Learning for Identification of MicroRNA Genes
Motivation: Numerous computational methodologies utilize techniques based on sequence conservation and/or structural similarity for microRNA gene prediction. In this study we describe a new technique, which is applicable across several species, for predicting microRNA genes. This technique is based on machine learning, using the Naïve Bayes classifier. This computational procedure automatically...
متن کاملCombining multi-species genomic data for microRNA identification using a Naïve Bayes classifier
MOTIVATION Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naive Bayes classifier. It automatically generates a model from the traini...
متن کاملWeed seeds identification by machine vision
The implementation of new methods for reliable and fast identification and classification of seeds is of major technical and economical importance in the agricultural industry. As in ocular inspection, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work, we assess the discriminating power of these characteristics for the unique...
متن کاملTree Kernel Usage in Naive Bayes Classifiers
We present a novel approach in machine learning by combining naı̈ve Bayes classifiers with tree kernels. Tree kernel methods produce promising results in machine learning tasks containing treestructured attribute values. These kernel methods are used to compare two tree-structured attribute values recursively. Up to now tree kernels are only used in kernel machines like Support Vector Machines o...
متن کاملTowards Biometric Person Identification using fNIRS
We investigate the potential of using fNIRS signals for biometric person identification. Independent sessions for training and testing have been recorded using 8 channels of frontal fNIRS. We extract logarithmic power spectral densities as features to train and test a Naı̈ve Bayes Classifier. We evaluate different frequency bands and report classification results for different trial lengths.
متن کامل