Proteomics: a theoretical platform for the analysis of animal protein sequence data – Fuzzy proteins are powerful and versatile molecular machines, and one of the key ingredients in protein synthesis is a set of proteins that represent a given protein structure. In this work, we present a method for fuzzy-protein synthesis that includes a set of fuzzy proteins as elements. This framework allows us to construct and understand fuzzy-protein clusters directly from fuzzy protein sequences. We present the algorithm which performs some experiments, including for the first time a complete description of a multi-dimensional fuzzy protein system, and demonstrate the effect the proposed method can have on the classification of protein sequences.
This paper proposes an efficient genetic algorithm for the identification of the molecular structure of a single protein. This algorithm has been tested on the problem of protein identification by means of molecular biology. This paper describes the proposed method, how the method is implemented, the procedure to test it and the experiments that it implements.
We propose a new framework for probabilistic inference from discrete data. This requires the assumption that the data are stable (i.e., it must be non-uniformly stable) and that the model is also non-differentiable. We then apply this criterion to a probabilistic model (e.g., a Gaussian kernel), in the model of the Kullback-Leibler equation, and show that the probabilistic inference from this model is equivalent to a probabilistic inference from two discrete samples. Our results are particularly strong in situations where the input data is correlated to the underlying distribution, while in other cases the data are not. Our framework is applicable to non-Gaussian distribution and it has strong generalization ability to handle data that is covariially random.
Robust Online Learning: A Nonparametric Eigenvector Approach
Proteomics: a theoretical platform for the analysis of animal protein sequence data
An Interactive Spatial-Directional RNN Architecture for the Pattern Recognition Challenge in the ASP
Dynamic Programming for Latent Variable Models in Heterogeneous DatasetsWe propose a new framework for probabilistic inference from discrete data. This requires the assumption that the data are stable (i.e., it must be non-uniformly stable) and that the model is also non-differentiable. We then apply this criterion to a probabilistic model (e.g., a Gaussian kernel), in the model of the Kullback-Leibler equation, and show that the probabilistic inference from this model is equivalent to a probabilistic inference from two discrete samples. Our results are particularly strong in situations where the input data is correlated to the underlying distribution, while in other cases the data are not. Our framework is applicable to non-Gaussian distribution and it has strong generalization ability to handle data that is covariially random.
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