APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

Chen, Huaming; Shen, Jun; Wang, Lei; Chi, Chi‐Hung

doi:10.1002/cpe.5846

Cited by 5 publications

(2 citation statements)

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“…Therefore, one of the imperative topics in every distributed system, similar to cloud and grid computing, is to find the appropriate services depending on the user's demand. To gain the best efficiency, a useful discovery, finding and selection of relevant services are necessary (Chen et al , 2020). However, to the best of our knowledge, a systematic and comprehensive survey about cloud service discovery is rare, at least in recent years.…”

Section: Introductionmentioning

confidence: 99%

Service discovery mechanisms in cloud computing: a comprehensive and systematic literature review

Heidari

Navimipour

2021

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PurposeThe main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.Design/methodology/approachSystematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.FindingsIn this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.Research limitations/implicationsThis research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.Practical implicationsOne of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.Originality/valueThis survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.

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Section: Introductionmentioning

confidence: 99%

Service discovery mechanisms in cloud computing: a comprehensive and systematic literature review

Heidari

Navimipour

2021

View full text Add to dashboard Cite

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“…Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. For this reason, Chen et al 2 proposed APEX2S, a novel two‐layer machine learning model, for discovery of the protein‐protein interactions data. APEX2S calibrated the focus for host‐pathogen protein‐protein interactions study, aiming to apply machine learning techniques for learning the interactions data and making predictions.…”

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confidence: 99%