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NEAT-FLEX: Predicting the conformational flexibility of amino acids using neuroevolution of augmenting topologies.

, and . J. Bioinform. Comput. Biol., 15 (3): 1750009:1-1750009:22 (2017)

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NEAT-FLEX: Predicting the conformational flexibility of amino acids using neuroevolution of augmenting topologies., and . J. Bioinform. Comput. Biol., 15 (3): 1750009:1-1750009:22 (2017)Predicting protein structural features with NeuroEvolution of Augmenting Topologies., and . IJCNN, page 873-880. IEEE, (2016)APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction., , , , and . Comput. Biol. Chem., (2015)Relevance aggregation for neural networks interpretability and knowledge discovery on tabular data., , and . Inf. Sci., (2021)CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research., , , and . J. Comput. Biol., 26 (4): 376-386 (2019)Neuroevolution of Neural Network Architectures Using CoDeepNEAT and Keras., , and . CoRR, (2020)Neuroevolution as a tool for microarray gene expression pattern identification in cancer research., , and . J. Biomed. Informatics, (2019)Multi-objective prioritization for data center vulnerability remediation., , , , , , , , , and 1 other author(s). CEC, page 1-8. IEEE, (2022)Microarray Classification and Gene Selection with FS-NEAT., , and . CEC, page 1-8. IEEE, (2018)