Structural Principles Analysis of Host-Pathogen Protein-Protein Interactions: A Structural Bioinformatics Survey

Shen

Concurrency and Computation

et al. 2020

Self Cite

Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud-based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host-pathogen protein-protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud-based multiomics data is discussed. In particular, a novel two-layer machine learning model, namely APEX2S, is proposed for discovery of the protein-protein interactions data. The results show that our model can better learn and predict from the accumulated host-pathogen protein-protein interactions.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shen

Concurrency and Computation

et al. 2020

Self Cite

“…There is a continuously broad research topic targeting on the mechanisms of infectious diseases [1,2,3]. These researches generally utilise the interaction data between host and pathogens, including proteins and genomes, to understand the theory of infectious diseases and anticipate to give effective solutions.…”

Section: Introductionmentioning

confidence: 99%

Leveraging SMOTE in a Two-Layer Model for Prediction of Protein-Protein Interactions

2019 Seventh International Conference on Advanced Cloud and Big Data (CBD)

Chi

et al. 2019

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The research of the mechanisms of infectious diseases between host and pathogens remains a hot topic. It takes stock of the interactions data between host and pathogens, including proteins and genomes, to facilitate the discoveries and prediction of underlying mechanisms. However, the incomplete proteinprotein interactions data impediment the advances in this exploration and solicit the wet-lab experiments to examine and verify the latent interactions. Although there have been numerous studies trying to leverage the computational models, especially machine learning models, the performances of these models were not good enough to produce high-fidelity candidates of interactions data due to the nature of the proteinprotein interactions data. In this paper, we propose a two-layer model for prediction of hostpathogen protein-protein interactions tackling the challenges affiliated to the feature representation algorithms and the imbalanced data. The twolayer model consists of two essential modules, which are XGBoost to reduce the imbalanced ratio of the data and SVM to improve the performance. SMOTE technology is incorporated as a key component in our model to alleviate the bias of imbalanced ratio. In this study, we have carefully collected proteins interactions data from public databases and built a dataset following the protocol with consensus of literature. A variety of models, including traditional models, models in major literature and our model, are verified on the datasets. Results demonstrate that our model significantly improve the performance comparing with the other state-of-the-art models.

“…It is witnessed that sequence data accumulated in a large scale via in vitro method from biology science is booming to challenge the traditional data service in cloud, not only from data volume aspect but also from data variety characteristic [2], [4]. The volume size benefits the analysis process while the variety of data sources deepens the understanding from these raw biological data [5] in proteomics area, where we consider both the protein-protein interactions (PPIs) and domain-domain interactions (DDI) as the extension components of biological sequence data service [6].…”

Section: Introductionmentioning

confidence: 99%

“…The transformation to knowledge stage delivers crucial insights in the omics study, specifically when focusing on host-pathogen protein interactions [7], [6]. In the postgenomics era, the omics study in host-pathogen interactions mostly relies on protein interactions analyses, which is named 'proteomics' [8].…”

Section: Introductionmentioning

confidence: 99%

Towards Biological Sequence Data Service with Insights

Shen

2018 IEEE International Conference on Big Data (Big Data)

et al. 2018

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Testable prediction outcomes generated by computational models based on available databases are the primary sources helping to design biological experiments. Although numerous databases have been designed by collecting data either only from literature manually or together with prediction outcomes from computational models, there is currently not a comprehensive data service framework delivering better insights for these results. In this paper, we introduce a biological sequence data service towards delivering deeper insights and helping better biological experiments design. The service includes following major components: a comprehensive database for storing biological data, data analytics tools for analysing biological data, and computational models for delivering testable prediction outcomes. Specifically, we present this service in a framework for studies on host-pathogen interactions. The design of this framework aims to improve the understanding of host-pathogen interactions. The relationships of hierarchical databases and their working mechanism, specifically between PPIs and DDIs, are also presented in this framework. Finally, the preliminary and practical experiences of building computational model for prediction is discussed.