A protein structural study based on the centrality analysis of protein sequence feature networks

In this paper, we use network approaches to analyze the relations between protein sequence features for the top hierarchical classes of CATH and SCOP. We use fundamental connectivity measures such as correlation (CR), normalized mutual information rate (nMIR), and transfer entropy (TE) to analyze the pairwise-relationships between the protein sequence features, and use centrality measures to analyze weighted networks constructed from the relationship matrices. In the centrality analysis, we find both commonalities and differences between the different protein 3D structural classes. Results show that all top hierarchical classes of CATH and SCOP present strong non-deterministic interactions for the composition and arrangement features of Cystine (C), Methionine (M), Tryptophan (W), and also for the arrangement features of Histidine (H). The different protein 3D structural classes present different preferences in terms of their centrality distributions and significant features.

Bipolar sorting and ranking of multistage alternatives

The present paper proposes an extension of the multicriteria Bipolar method, introduced by E. Konarzewska-Gubała, and its application to the control of multistage, multicriteria decision processes with a fixed number of stages. At each stage, two sets of reference points are determined, which constitute a reference system for the evaluation of stage decisions. At the end of the process, multistage alternatives—compositions of stage alternatives—are evaluated. The procedure proposed, which includes elements of the Electre methodology, allows to assign each multistage alternative to one of the six predefined, hierarchical classes, and then to perform ranking within each class. The purpose of the paper is to present and substantiate the dynamic Bipolar procedure. An essential part of the paper is a numerical example which illustrates the notions and relationships introduced.

Multiobjective dynamic programming in bipolar multistage method

The multicriteria Bipolar method can be extended and used to control multicriteria, multistage decision processes. In this extension, at each stage of the given multistage process two sets of reference points are determined, constituting a reference system for the evaluation of stage alternatives. Multistage alternatives, which are compositions of stage alternatives, are assigned to one of six predefined hierarchical classes and then ranked. The aim of this paper is to show the possibility of finding the best multistage alternative, using Bellman’s optimality principle and optimality equations. Of particular importance is a theorem on the non-dominance of the best multistage alternative, proven here. The methodology proposed allows to avoid reviewing each multistage alternative, which is important in large-size problems. The method is illustrated by a numerical example and a brief description of the sustainable regional development problem. The problem can be solved by means of the proposed procedure.

Do Handcycling Time-Trial Velocities Achieved by Para-Cycling Athletes Vary Across Handcycling Classes?

The classification system for handcycling groups athletes into five hierarchical classes, based on how much their impairment affects performance. Athletes in class H5, with the least impairments, compete in a kneeling position, while athletes in classes H1 to H4 compete in a recumbent position. This study investigated the average time-trial velocity of athletes in different classes. A total of 1,807 results from 353 athletes who competed at 20 international competitions (2014–2018) were analyzed. Multilevel regression was performed to analyze differences in average velocities between adjacent pairs of classes, while correcting for gender, age, and event distance. The average velocity of adjacent classes was significantly different (p < .01), with higher classes being faster, except for H4 and H5. However, the effect size of the differences between H3 and H4 was smaller (d = 0.12). Hence, results indicated a need for research in evaluating and developing evidence-based classification in handcycling, yielding a class structure with meaningful performance differences between adjacent classes.

Hierarchical Feature Selection with Recursive Regularization

In the big data era, the sizes of datasets have increased dramatically in terms of the number of samples, features, and classes. In particular, there exists usually a hierarchical structure among the classes. This kind of task is called hierarchical classification. Various algorithms have been developed to select informative features for flat classification. However, these algorithms ignore the semantic hyponymy in the directory of hierarchical classes, and select a uniform subset of the features for all classes. In this paper, we propose a new technique for hierarchical feature selection based on recursive regularization. This algorithm takes the hierarchical information of the class structure into account. As opposed to flat feature selection, we select different feature subsets for each node in a hierarchical tree structure using the parent-children relationships and the sibling relationships for hierarchical regularization. By imposing $\ell_{2,1}$-norm regularization to different parts of the hierarchical classes, we can learn a sparse matrix for the feature ranking of each node. Extensive experiments on public datasets demonstrate the effectiveness of the proposed algorithm.