An Experimental Comparison of Classification Algorithms for the Hierarchical Prediction of Protein Function
نویسندگان
چکیده
1 Computing Laboratory and Centre for BioMedical Informatics, University of Kent, Canterbury, CT2 7NF, UK Email: {a.d.secker, a.a.freitas}@kent.ac.uk 2 Edward Jenner Institute, Compton, Newbury, Berkshire, RG20 7NN, UK Email: [email protected], [email protected] 3 Departments of Computer Science and Electronics, University of York, Heslington, YO10 5DD, UK Email: {jtimmis, miguel}@cs.york.ac.uk
منابع مشابه
Spectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms
Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. In the proposed method ten spatial/texture features, using mean, standard deviation, contrast, homogeneity, corr...
متن کاملPrediction of Protein Sub-Mitochondria Locations Using Protein Interaction Networks
Background: Prediction of the protein localization is among the most important issues in the bioinformatics that is used for the prediction of the proteins in the cells and organelles such as mitochondria. In this study, several machine learning algorithms are applied for the prediction of the intracellular protein locations. These algorithms use the features extracted from pro...
متن کاملNew Ant Colony Optimisation Algorithms for Hierarchical Classification of Protein Functions
Ant colony optimisation (ACO) is a metaheuristic to solve optimisation problems inspired by the foraging behaviour of ant colonies. It has been successfully applied to several types of optimisation problems, such as scheduling and routing, and more recently for the discovery of classification rules. The classification task in data mining aims at predicting the value of a given goal attribute fo...
متن کاملAn Experimental Comparison of Hierarchical Bayes and True Path Rule Ensembles for Protein Function Prediction
The computational genome-wide annotation of gene functions requires the prediction of hierarchically structured functional classes and can be formalized as a multiclass, multilabel, multipath hierarchical classification problem, characterized by very unbalanced classes. We recently proposed two hierarchical protein function prediction methods: the Hierarchical Bayes (hbayes) and True Path Rule ...
متن کاملMental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...
متن کاملComparison of Genetic and Hill Climbing Algorithms to Improve an Artificial Neural Networks Model for Water Consumption Prediction
No unique method has been so far specified for determining the number of neurons in hidden layers of Multi-Layer Perceptron (MLP) neural networks used for prediction. The present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. The data used in the present research for prediction are consumption data of water...
متن کامل