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Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Systematic Evaluation Of Machine-Learning Methods For Identifying Human-Pathogen Protein-Protein Interactions, Huaming Chen, Fuyi Li, Lei Wang, Yaochu Jin, Chi-Hung Chi, Lukasz Kurgan, Jiangning Song, Jun Shen
Systematic Evaluation Of Machine-Learning Methods For Identifying Human-Pathogen Protein-Protein Interactions, Huaming Chen, Fuyi Li, Lei Wang, Yaochu Jin, Chi-Hung Chi, Lukasz Kurgan, Jiangning Song, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein–protein interaction identification, including human–pathogen protein–protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human–bacterium protein–protein interactions (HB-PPIs). We first reviewed …
Attention-Based High-Order Feature Interactions To Enhance The Recommender System For Web-Based Knowledge-Sharing Servic, Jiayin Lin, Geng Sun, Jun Shen, Tingru Cui, David Pritchard, Dongming Xu, Li Li, Wei Wei, Ghassan Beydoun, Shiping Chen
Attention-Based High-Order Feature Interactions To Enhance The Recommender System For Web-Based Knowledge-Sharing Servic, Jiayin Lin, Geng Sun, Jun Shen, Tingru Cui, David Pritchard, Dongming Xu, Li Li, Wei Wei, Ghassan Beydoun, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender …
A Framework Towards Data Analysis On Host-Pathogen Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
A Framework Towards Data Analysis On Host-Pathogen Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Jiangning Song
Faculty of Engineering and Information Sciences - Papers: Part B
With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein …