Abdolkarim Elahi

In protein interaction networks, a complex is a group of proteins that causes a biological process to take place. The correct identification of complexes can help to better understand function of cells used for therapeutic purposes, such as drug discoveries. This paper uses core-attachment structure, centrality measures, and biological properties of proteins to identify protein complex with the aim of enhancing prediction accuracy compared to related work. We used the inherent organization of complex to the identification in this article, while most methods have not considered such properties. On the other hand, clustering methods, as the common method for identifying complexes in protein interaction networks have been applied. However, we want to propose a method for more accurate identification of complexes in this article. Using this method, we determined the core center of each complex and its attachment proteins using the centrality measures, biological properties and weight density, whereby the weight of each interaction was calculated using the protein information in the gene ontology. In the proposed approach to weighting the network and measuring the importance of proteins, we used our previous work. To compare with other methods, we used datasets DIP, Collins, Krogan, and Human. The results show that the performance of our method was significantly improved, compared to other methods, in terms of detecting the protein complex. Using the p-value concept, we show the biological significance of our predicted complexes. The proposed method could identify an acceptable number of protein complexes, with the highest proportion of biological significance in collaborating on the functional annotation of proteins.

The identification of essential proteins in protein–protein interaction (PPI) networks is not only important in understanding the process of cellular life but also useful in diagnosis and drug design. The network topology‐based centrality measures are sensitive to noise of network. Moreover, these measures cannot detect low‐connectivity essential proteins. The authors have proposed a new method using a combination of topological centrality measures and biological features based on statistical analyses of essential proteins and protein complexes. With incomplete PPI networks, they face the challenge of false‐positive interactions. To remove these interactions, the PPI networks are weighted by gene ontology. Furthermore, they use a combination of classifiers, including the newly proposed measures and traditional weighted centrality measures, to improve the precision of identification. This combination is evaluated using the logistic regression model in terms of significance levels. The proposed method has been implemented and compared to both previous and more recent efficient computational methods using six statistical standards. The results show that the proposed method is more precise in identifying essential proteins than the previous methods. This level of precision was obtained through the use of four different data sets: YHQ‐W, YMBD‐W, YDIP‐W and YMIPS‐W.

Nowadays, software development processes are usually based on methods which use styles of software architecture. Indeed, software development styles differently support different quality features. In architecture styles, tactics are used for achieving quality features. Stakeholders usually request several quality features simultaneously. In this paper, a framework has been proposed in which tactics are first used to evaluate and compare the architectures with regard to only one quality feature. Then, the Analytical Hierarchy Process of Fuzzy Integral used to enhance architecture selection accuracy which is based on all the requests of Stakeholders. Using the fuzzy hierarchical process, we considered the Stakeholders' requests for quality features in the process of selecting the desirable style based on 4 factors: (1) dependency between the features in determining their weight, (2) contrast between the features (by a negative impact), (3) interaction between the features (by a positive impact), and (4) sorting out ambiguities (clarifying ambiguous and unknown values). Since Stakeholders are highly sensitive to the quality features of their requirements, our contribution in this paper is a method to select the architecture styles based on the features.