The COVID-19 pandemic caused by SARS-CoV-2 has emphasized the importance of studying virus-host protein-protein interactions (PPIs) and drug-target interactions (DTIs) to discover effective antiviral drugs. While several computational algorithms have been developed for this purpose, most of them overlook the interplay pathways during infection along PPIs and DTIs. In this paper, we present a novel multipartite graph learning approach to uncover hidden binding affinities in PPIs and DTIs. Our method leverages a comprehensive biomolecular mechanism network that integrates protein-protein, genetic, and virus-host interactions, enabling us to learn a new graph that accurately captures the underlying connected components. Notably, our method identifies clustering structures directly from the new graph, eliminating the need for post-processing steps. To mitigate the detrimental effects of noisy or outlier data in sparse networks, we propose a robust objective function that incorporates the $\ell_{2,p}$-norm and a constraint based on the pth-order Ky-Fan norm applied to the graph Laplacian matrix. Additionally, we present an efficient optimization method tailored to our framework. Experimental results demonstrate the superiority of our approach over existing state-of-the-art techniques, as it successfully identifies potential repurposable drugs for SARS-CoV-2, offering promising therapeutic options for COVID-19 treatment.

Chest X-rays are commonly used for diagnosing and characterizing lung diseases, but the complex morphological patterns in radiographic appearances can challenge clinicians in making accurate diagnoses. To address this challenge, various learning methods have been developed for algorithm-aided disease detection and automated diagnosis. However, most existing methods fail to account for the heterogeneous variability in longitudinal imaging records and the presence of missing or inconsistent temporal data. In this paper, we propose a novel longitudinal learning framework that enriches inconsistent imaging data over sequential time points by leveraging two-dimensional principal component analysis and a robust adaptive loss function. We also derive an efficient solution algorithm that ensures both objective and sequence convergence for the nonconvex optimization problem. Our experiments on the CheXpert dataset demonstrate improved performance in capturing indicative abnormalities in medical images and achieving satisfactory diagnoses. We believe that our method will be of significant interest to the research community working on medical image analysis.