One of the challenging problems in bioinformatics is the prediction of protein function. Protein function is the main key that can be used to classify different proteins. Protein function can be inferred experimentally with very small throughput or computationally with very high throughput. Computational methods are sequence based or structure based. Structure-based methods produce more accurate protein function prediction. In this article, we propose a new protein structure representation for efficient protein function prediction. The representation is based on three-dimensional patterns of protein residues. In the analysis, we used protein function based on enzyme activity through six mechanistically diverse enzyme superfamilies: amidohydrolase, crotonase, haloacid dehalogenase, isoprenoid synthase type I, and vicinal oxygen chelate. We applied three different classification methods, naïve Bayes, k-nearest neighbors, and random forest, to predict the enzyme superfamily of a given protein. The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns. The results show that the proposed representation achieved prediction accuracy up to 98%, with improvement of about 10% on average.
Recently, testing mobile applications is gaining much attention due to the widespread of smartphones and the tremendous number of mobile applications development. It is essential to test mobile applications before being released for the public use. Graphical user interface (GUI) testing is a type of mobile applications testing conducted to ensure the proper functionality of the GUI components. Typically, GUI testing requires a lot of effort and time whether manual or automatic. Cloud computing is an emerging technology that can be used in the software engineering field to overcome the defects of the traditional testing approaches by using cloud computing resources. As a result, testing‐as‐a‐service is introduced as a service model that conducts all testing activities in a fully automated manner. In this paper, a system for mobile applications GUI testing based on testing‐as‐a‐service architecture is proposed. The proposed system performs all testing activities including automatic test case generation and simultaneous test execution on multiple virtual nodes for testing Android‐based applications. The proposed system reduces testing time and meets fast time‐to market constraint of mobile applications. Moreover, the proposed system architecture addresses many issues such as maximizing resource utilization, continuous monitoring to ensure system reliability, and applying fault‐tolerance approach to handle occurrence of any failure.
The large amounts of available protein structures emerges the need for computational methods for protein function prediction. Predicting protein function is mainly based on finding similarities between proteins with unknown function with already annotated proteins. This may be achieved using different protein characteristics: sequences, interactions, localization, structure and or psychochemical. A lot of review papers mainly focus on sequence and psychochemical featuresbased methods. This is because sequence and psychochemical data are easy to deal with and to interpret the results, and much available compared to protein structures. However, structure-based computational methods provide additional accuracy and reliability of protein function prediction. Therefore, unlike many review papers, this paper presents an up-to-date review on the structure-based protein function prediction. The aim was to provide a recent and comprehensive review of protein structure related topics: function aspects, structural classification, databases, tools and methods.
Cloud testing is gaining much attention in both academia and industry as an emerging concept in the field of software testing. Cloud testing implies leveraging the resources of the cloud computing environment to overcome deficiencies of the traditional testing approaches. As a result, testing-as-a-service (TaaS) is introduced as a service model that conducts all testing activities in a fully automated manner using cloud-based resources. Performance testing is a type of software testing that validates the performance characteristics of the application under test (AUT) when subjected to different workloads during its operation. Performance characteristics include throughput, response time, and resource utilization of the AUT under a certain workload. This paper focuses on reviewing the literature related to the provision of performance testing as a service (P-TaaS). In this study, we survey the previous work related to cloud-based performance testing. We show the strengths and weaknesses of the current research. Besides, we compare the P-TaaS with the traditional performance testing methodologies. A detailed discussion of the benefits and challenges of P-TaaS is introduced along with identifying the research gaps and the future directions that can be adopted.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.