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Prediction of protein fold families remains an existing challenge in molecular biology and bioinformatics, mainly because proteins form a broad range of complex three-dimensional configurations and because the number of proteins registered in datasets has dramatically increased in the recent years. Computational alternatives must then be designed for substituting experimental methods. However, implementations...
Many proteins can interact with other proteins to perform specific functions. Predicting those interactions is important in order to analyze signaling pathways or to define the influence of a specific protein in some diseases. This work proposes the implementation of Support Vector Machines (SVM) for the prediction of protein-protein interactions using physical-chemical features taken from AA index...
In this paper, a SVM-based method is implemented for the prediction of protein-protein interactions. This model is initially trained with a set of over 69.000 pairs of protein sequences based on documented positive interactions. Then, a cross-validation method is performed for estimating the accuracy of the system, showing acceptable performances in terms of sensitivity, specificity and geometric...
This work implements a type of string kernel called Mismatch kernel, together with a methodology involving Support Vector Machines (SVM) for solving 14 molecular function classification problems of land plants (Embryophyta). The implemented methodology uses metaheuristic bio-inspired algorithms for finding optimal hyperparameters of the SVM, to solve the problem of imbalanced data class weights are...
Nowadays, the use of Wearable User Interfaces has been extensively growing in medical monitoring applications. However, production and manufacture of prototypes without automation tools may lead to non viable results since it is often common to find an optimization problem where several variables are in conflict with each other. Thus, it is necessary to design a strategy for balancing the variables...
A comparative analysis of four multi-label classification methods is performed in order to determine the best topology for the problem of protein function prediction, using support vector machines as base classifiers. Comparisons are done in terms of performance and computational cost of parallelized versions of the algorithms, for determining its applicability in high-throughput scenarios. Results...
Predicting the localization of a protein has become a useful practice for inferring its function. Most of the reported methods to predict subcellular localizations in Gram-negative bacterial proteins have shown a low false positive rate. However, some subcellular compartmens like “periplasm” and “extracellular medium” are difficult to predict and remain high false negative rates. In this paper, a...
The functional prediction of proteins is one of main purposes of computational biology. Many techniques have been developed to solve this problem. Methods based on alignments of sequences like BLASTP are the most commonly used. However, these techniques have been criticized due to their on failures detecting homologous sequences under some identity thresholds between sequences. Although this is an...
Predicting the sub-cellular localization of a protein can provide useful information to uncover its molecular functions. In this sense, numerous prediction techniques have been developed, which usually have been focused on global information of the protein or sequence alignments. However, several studies have shown that the functional nature of proteins is ruled by conserved sub-sequence patterns...
Predict the function of unknown proteins is one of the principal goals in computational biology. The subcellular localization of a protein allows further understanding its structure and molecular function. Numerous prediction techniques have been developed, usually focusing on global information of the protein. But, predictions can be done through the identification of functional sub-sequence patterns...
Estimating the function of unknown proteins is one of the main goals in bioinformatics. In the last few years, many pattern recognition algorithms have been developed, usually focusing on global information of the protein. Conversely, predictions can be done through the identification of functional sub-sequence patterns or motifs, but most methods for motifs detection require a predefined fixed window...
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