A time evolving online social network generation algorithm.

The rapid growth of online social media usage in our daily lives has increased the importance of analyzing the dynamics of online social networks. However, the dynamic data of existing online social media platforms are not readily accessible. Hence, there is a necessity to synthesize networks emulating those of online social media for further study. In this work, we propose an epidemiology-inspired and community-based, time-evolving online social network generation algorithm (EpiCNet), to generate a time-evolving sequence of random networks that closely mirror the characteristics of real-world online social networks. Variants of the algorithm can produce both undirected and directed networks to accommodate different user interaction paradigms. EpiCNet utilizes compartmental models inspired by mathematical epidemiology to simulate the flow of individuals into and out of the online social network. It also employs an overlapping community structure to enable more realistic connections between individuals in the network. Furthermore, EpiCNet evolves the community structure and connections in the simulated online social network as a function of time and with an emphasis on the behavior of individuals. EpiCNet is capable of simulating a variety of online social networks by adjusting a set of tunable parameters that specify the individual behavior and the evolution of communities over time. The experimental results show that the network properties of the synthetic time-evolving online social network generated by EpiCNet, such as clustering coefficient, node degree, and diameter, match those of typical real-world online social networks such as Facebook and Twitter.

Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.

BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.

Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 -human interactome.

Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dramatically change clinical phenotypes of the virus, including transmission rates and severity of the disease. On the other hand, highly dissimilar virus families such as Coronaviridae, Ebola, and HIV have overlap in functions. In this work we aim to analyze the role of protein sequence in the binding of SARS-CoV-2 virus proteins towards human proteins and compare it to that of the above other viruses. We build supervised machine learning models, using Generalized Additive Models to predict interactions based on sequence features and find that our models perform well with an AUC-PR of 0.65 in a class-skew of 1:10. Analysis of the novel predictions using an independent dataset showed statistically significant enrichment. We further map the importance of specific amino-acid sequence features in predicting binding and summarize what combinations of sequences from the virus and the host is correlated with an interaction. By analyzing the sequence-based embeddings of the interactomes from different viruses and clustering them together we find some functionally similar proteins from different viruses. For example, vif protein from HIV-1, vp24 from Ebola and orf3b from SARS-CoV all function as interferon antagonists. Furthermore, we can differentiate the functions of similar viruses, for example orf3a's interactions are more diverged than orf7b interactions when comparing SARS-CoV and SARS-CoV-2.