The paper focuses on the problem of unpredictable oscillations for the Hopfield-type neural networks. Since the unpredictable dynamics is associated with Poincaré chaos, the importance of the motions is indisputable for problems of artificial intelligence and deep learning. The presence of chaos in each coordinate of the state space is productive in applied problems. This is why, we consider the phenomenon of the unpredictability for each coordinate of the network. The theoretical results have been illustrated with numerical analysis.
Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype–phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non–disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype–phenotype correlation methods by using the wealth of protein structural information.
Radial Basis FunctionRadial basis function networks, commonly known as RBF, can also be employed in almost every kind of problems solved by MLPs, including those involving curve fitting and pattern classification.
