Jorge Alberto Jaramillo-Garzón

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Proteins are the key elements on the path from genetic information to the development of life. The roles played by the different proteins are difficult to uncover experimentally as this process involves complex procedures such as genetic modifications, injection of fluorescent proteins, gene knock-out methods and others. The knowledge learned from each(More)
Learning from imbalanced data sets presents an important challenge to the machine learning community. Traditional classification methods, seeking to minimize the overall error rate of the whole training set, do not perform well on imbalanced data since they assume a relatively balanced class distribution and put too much strength on the majority class. This(More)
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(More)
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(More)
An analysis of the predictability of subcellular locations is performed by using simple pattern recognition techniques in an attempt to capture the real dimensions of the problem at hand. Results show that there are some particular locations that does not need of high complexity classification models to be predicted with high accuracies, and some partial(More)
This paper presents a comparison of three strategies for managing the imbalance problem: undersampling, SMOTE and Weighted SVM. Undersampling is a strategy where the samples of the majority class are discarded; SMOTE (Synthetic Minority Over-sampling Technique) is a method in which synthetic samples of the minority class are added to the dataset; Weighted(More)