Rafał Adamczak

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Accurate prediction of relative solvent accessibilities (RSAs) of amino acid residues in proteins may be used to facilitate protein structure prediction and functional annotation. Toward that goal we developed a novel method for improved prediction of RSAs. Contrary to other machine learning-based methods from the literature, we do not impose a(More)
Owing to the use of evolutionary information and advanced machine learning protocols, secondary structures of amino acid residues in proteins can be predicted from the primary sequence with more than 75% per-residue accuracy for the 3-state (i.e., helix, beta-strand, and coil) classification problem. In this work we investigate whether further progress may(More)
A framework for Similarity-Based Methods (SBMs) includes many classification models as special cases: neural network of the Radial Basis Function Networks type, Feature Space Mapping neurofuzzy networks based on separable transfer functions, Learning Vector Quantization, variants of the k nearest neighbor methods and several new models that may be presented(More)
Despite all the progress in neural networks field the technology is brittle and sometimes difficult to apply. Good initialization of adaptive parameters in neural networks and optimization of architecture are the key factor to create robust neural networks. Methods of initialization of MLPs are reviewed and new methods based on clusterization techniques are(More)
Methodology of extraction, optimization and application of sets of logical rules is described. The three steps are largely independent. Neural networks are used for initial rule extraction, local or global minimization procedures for optimization, and Gaussian uncertainties of measurements are assumed during application of logical rules. A tradeoff between(More)
Algorithms for extraction of logical rules from data that contains real-valued components require determination of linguistic variables or membership functions. Context-dependent membership functions for crisp and fuzzy linguistic variables are introduced and methods of their determination described. Methodology of extraction, optimization and application(More)
Initialization of adaptive parameters in neural networks is of crucial importance to the speed of convergence of the learning procedure. Methods of initialization for the density networks are reviewed and two new methods, based on decision trees and dendrograms, presented. These two methods were applied in the Feature Space Mapping framework to artificial(More)
A comprehensive methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multi-layered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming(More)