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 interactions, which represented the primary challenges associated with infectious diseases and drug design. Accordingly, a novel framework based on data analytics and machine learning techniques is detailed for analyzing these areas and we will describe the analytical results from host–pathogen protein–protein interactions (HP-PPI). Based on this framework, which serves as a pipeline solution for extracting and learning from the raw proteomics data, we have firstly evaluated several models from literature using different analytic technologies and performance measurements. An unsupervised deep learning model based on stacked denoising autoencoders, is subsequently proposed to capture higher level feature regarding the sequence information in the framework. The achieved performance indicates a superior capability of the unsupervised deep learning model in dealing with the host–pathogen protein interactions scenario among all of these models. The results will further help to enrich a theoretical and technical foundation for analyzing HP-PPI networks.