María N. Moreno García

Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks

Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%.

Feature extraction using clustering of protein

In this paper we investigate the usage of a clustering algorithm as a feature extraction technique to find new features to represent the protein sequence. In particular, our work focuses on the prediction of HIV protease resistance to drugs. We use a biologically motivated similarity function based on the contact energy of the amino acid and the position in the sequence. The performance measure was computed taking into account the clustering reliability and the classification validity. An SVM using 10-fold crossvalidation and the k-means algorithm were used for classification and clustering respectively. The best results were obtained by reducing an initial set of 99 features to a lower dimensional feature set of 36-66 features.

Combining Concept Maps and Petri Nets to Generate Intelligent Tutoring Systems: A Possible Approach

The use of pedagogical methods with the technologies of the information and communications produces a new quality that favours the task of generating, transmitting and sharing knowledge. Such is the case of the pedagogical effect that produces the use of the Concept Maps, which constitute a tool for the management of knowledge, an aid to personalize the learning process, to exchange knowledge, and to learn how to learn. In this paper the authors present a new approach to elaborate Intelligent Tutoring Systems, where the techniques of Concept Maps and Artificial Intelligence are combined, using Petri Nets as theoretical frame, for the student model. The pedagogical model that controls the interaction between the apprentice and the generated Intelligent Tutoring Systems is implemented by Petri Nets. The Petri Nets transitions are controlled by conditions that refer to the apprentice model. The firing of these transitions produces actions that update this apprentice model. These conditions are automatically included into the pedagogical model and the teacher has only to specify the contents of the domain model.