Wyniki wyszukiwania - BazTech

1

Virus–human protein–protein interaction prediction using Bayesian matrix factorization and projection techniques

Nourani E., Khunjush F., Sevilgen F. E.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

574--585

EN

Pathogens infect host organisms by exploiting host cellular mechanisms and evading host defence mechanisms through molecular pathogen–host interactions (PHIs). Discovering new interactions between pathogen and human proteins is very crucial in understanding the infection mechanisms. By analysing interaction networks, the interactions responsible for infectious diseases can be detected and new drugs disabling these interactions can be delivered. In this paper, we propose a method based on Bayesian matrix factorization for predicting PHIs along with a projection-based technique and combine the results by employing an ensemble method. Furthermore, two features, target similarity and attacker similarity, are utilized for the first time in the literature for PHI prediction. The advantages of the proposed methods are two folds. Firstly, they relieve the need for negative samples which is significant since there is no available dataset providing negative samples for most of the pathogenic systems. Secondly, the experiments demonstrate that the proposed approach outperforms state-of-the-art methods; roughly 20% of top 50 predictions are among recently validated interactions. So, the search space for wet-lab experiments to obtain validated interactions can be considerably narrowed down from a huge number of possible interactions.

2

Application of sparse linear discriminant analysis for prediction of protein-protein interactions

Stąpor K., Fabian P.

Information Systems in Management

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2016

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Vol. 5, No. 1

109--118

EN

To understand the complex cellular mechanisms involved in a biological system, it is necessary to study protein-protein interactions (PPIs) at the molecular level, in which prediction of PPIs plays a significant role. In this paper we propose a new classification approach based on the sparse discriminant analysis [10] to predict obligate (permanent) and non-obligate (transient) protein-protein interactions. The sparse discriminant analysis [10] circumvents the limitations of the classical discriminant analysis [4, 9] in the high dimensional low sample size settings by incorporating inherently the feature selection into the optimization procedure. To characterize properties of protein interaction, we proposed to use the binding free energies. The performance of our proposed classifier is 75% ± 5%.

3

Renesans peptydów a nowe cele terapeutyczne

Krzywik J., Katarzyńska J.

Eliksir

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2015

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nr 2

15--22

4

Keram: a novel stand-alone application for correlated mutations identification and analysis

Kuśka J., Leluk J., Lesyng B.

Bio-Algorithms and Med-Systems

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2011

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Vol. 7, no. 1

71--76

EN

Keram is a stand-alone Windows 2000/XP/Vista application designed for the detection and analysis of the correlated mutations. Study of this phenomenon provides important information about protein structure stability factors as well as the formation of protein complexes. It is generally assumed that the mechanism of compensation explains the mutations that occur simultaneously. Keram is designed to detect the mutational correlations by comparative analysis of multiple sequence alignments. Additionally a three dimensional structure can be applied to calculate the distance between correlated positions in the protein molecule. Keram has been succesfully applied for the analysis of kinase subfamilies. The obtained data suggest that the mechanism of compensation does not explain utterly this phenomenon which seems to be much more complex and diverse. The residues that are detected as correlated are often placed at very distant positions of the protein structure, therefore the direct mutual interaction between them is impossible. We have detected not only correlated pairs, but also clusters of positions (even more than 10) that reveal correlated changeability.