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  1. Abstract Summary

    Computational methods to predict protein–protein interaction (PPI) typically segregate into sequence-based ‘bottom-up’ methods that infer properties from the characteristics of the individual protein sequences, or global ‘top-down’ methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. We thus introduce Topsy-Turvy, a method that newly synthesizes both views in a sequence-based, multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by incorporating patterns from both global and molecular-level views of protein interaction. In a cross-species context, we show it achieves state-of-the-art performance, offering the ability to perform genome-scale, interpretable PPI prediction for non-model organisms with no existing experimental PPI data. In species with available experimental PPI data, we further present a Topsy-Turvy hybrid (TT-Hybrid) model which integrates Topsy-Turvy with a purely network-based model for link prediction that provides information about species-specific network rewiring. TT-Hybrid makes accurate predictions for both well- and sparsely-characterized proteins, outperforming both its constituent components as well as other state-of-the-art PPI prediction methods. Furthermore, running Topsy-Turvy and TT-Hybrid screens ismore »feasible for whole genomes, and thus these methods scale to settings where other methods (e.g. AlphaFold-Multimer) might be infeasible. The generalizability, accuracy and genome-level scalability of Topsy-Turvy and TT-Hybrid unlocks a more comprehensive map of protein interaction and organization in both model and non-model organisms.

    Availability and implementation

    https://topsyturvy.csail.mit.edu.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

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  2. Abstract Motivation

    Protein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein–protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein–protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties.

    Results

    GLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein–protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein–protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein–protein association networks. Furthermore, we introduce the GLIDER graph neighborhoodmore »as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson’s Disease GWAS genes, rediscover many genes which have known involvement in Parkinson’s disease pathways, plus suggest some new genes to study.

    Availability and implementation

    All code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

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  3. Various algorithmic and statistical approaches have been proposed to uncover functionally coherent network motifs consisting of sets of genes that may occur as compensatory pathways (called Between Pathway Modules, or BPMs) in a high-throughput S. Cerevisiae genetic interaction network. We extend our previous Local-Cut/Genecentric method to also make use of a spectral clustering of the physical interaction network, and uncover some interesting new fault-tolerant modules.
    Free, publicly-accessible full text available August 7, 2023
  4. Free, publicly-accessible full text available August 7, 2023
  5. Remote scientific collaborations have been pivotal in generating scientific discoveries and breakthroughs that accelerate research in many fields. Emerging VR applications for remote work, which utilize commercially available head-mounted displays (HMDs), offer the promise to enhance collaboration, through spatial and embodied experiences. However, there is little evidence on how professionals in general, and scientists in particular, could use existing commercial VR applications to support their cognitive and creative collaborative processes while exploring real-world data as part of day-to-day collaborative work. In this paper, we present findings from an empirical study with 14 coral reef scientists, examining how they chose to utilize available resources in existing virtual environments for their ongoing data-driven collaborative research. We shed light on scientists’ data organization practices, identify affordances unique to VR for supporting cognition in a collaborative setting, and highlight design requirements for supporting cognitive and creative collaboration processes in future tools.
    Free, publicly-accessible full text available June 20, 2023
  6. Free, publicly-accessible full text available May 1, 2023
  7. Diffusion State Distance (DSD) is a data-dependent metric that compares data points using a data-driven diffusion process and provides a powerful tool for learning the underlying structure of high-dimensional data. While finding the exact nearest neighbors in the DSD metric is computationally expensive, in this paper, we propose a new random-walk based algorithm that empirically finds approximate k-nearest neighbors accurately in an efficient manner. Numerical results for real-world protein-protein interaction networks are presented to illustrate the efficiency and robustness of the proposed algorithm. The set of approximate k-nearest neighbors performs well when used to predict proteins’ functional labels.
    Free, publicly-accessible full text available March 18, 2023
  8. Free, publicly-accessible full text available March 1, 2023
  9. Cremonini, Marco (Ed.)
    Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade modelmore »with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and “alternative facts.”« less