International Journal of Man-Machine Studies
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Swarm intelligence
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Geno2pheno: Interpreting Genotypic HIV Drug Resistance Tests
IEEE Intelligent Systems
Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
A fuzzy cognitive maps modeling, learning and simulation framework for studying complex system
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Expert Systems with Applications: An International Journal
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The complex dynamics of Human Immunodeficiency Virus leads to serious problems on predicting the drug resistance. Several machine learning techniques have been proposed for modelling this classification problem, but most of them are difficult to aggregate and interpret. In fact, in last years the protein modelling of this virus has become, from diverse points of view, an open problem for researchers. This paper presents a modelling of the protease protein as a dynamic system through Fuzzy Cognitive Maps, using the amino acids contact energies for the sequence description. In addition, a learning scheme based on swarm intelligence called PSO-RSVN is used to estimate the causal weight matrix that characterizes these structures. Finally, an aggregation procedure with previously adjusted maps is applied for obtaining a prototype map, in order to discover knowledge in the causal influences, and simulate the system behaviour when a single (or multiple) mutation takes place.