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1. Bayesian graphical models for modern biological applications

   https://doi.org/10.1007/s10260-021-00572-8  

   Ni, Yang; Baladandayuthapani, Veerabhadran; Vannucci, Marina; Stingo, Francesco C. (May 2021, Statistical Methods & Applications)

   Abstract Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging. 

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2. Latent Model-Based Clustering for Biological Discovery

   https://doi.org/https://doi.org/10.1016/j.isci.2019.03.018  

   Bing, X; Bunea, F; Royer, M; Das, J. (March 2019, iScience)

   LOVE, a robust, scalable latent model-based clustering method for biological discovery, can be used across a range of datasets to generate both overlapping and non-overlapping clusters. In our formulation, a cluster comprises variables associated with the same latent factor and is determined from an allocation matrix that indexes our latent model. We prove that the allocation matrix and corresponding clusters are uniquely defined. We apply LOVE to biological datasets (gene expression, serological responses measured from HIV controllers and chronic progressors, vaccine-induced humoral immune responses) resulting in meaningful biological output. For all three datasets, the clusters generated by LOVE remain stable across tuning parameters. Finally, we compared LOVE's performance to that of 13 state-of-the-art methods using previously established benchmarks and found that LOVE outperformed these methods across datasets. Our results demonstrate that LOVE can be broadly used across large-scale biological datasets to generate accurate and meaningful overlapping and non-overlapping clusters. 

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3. Genomic Tools in Biological Invasions: Current State and Future Frontiers

   https://doi.org/10.1093/gbe/evad230  

   McGaughran, Angela; Dhami, Manpreet K; Parvizi, Elahe; Vaughan, Amy L; Gleeson, Dianne M; Hodgins, Kathryn A; Rollins, Lee A; Tepolt, Carolyn K; Turner, Kathryn G; Atsawawaranunt, Kamolphat; et al (January 2024, Genome Biology and Evolution)

   Human activities are accelerating rates of biological invasions and climate-driven range expansions globally, yet we understand little of how genomic processes facilitate the invasion process. Although most of the literature has focused on underlying phenotypic correlates of invasiveness, advances in genomic technologies are showing a strong link between genomic variation and invasion success. Here, we consider the ability of genomic tools and technologies to (i) inform mechanistic understanding of biological invasions and (ii) solve real-world issues in predicting and managing biological invasions. For both, we examine the current state of the field and discuss how genomics can be leveraged in the future. In addition, we make recommendations pertinent to broader research issues, such as data sovereignty, metadata standards, collaboration, and science communication best practices that will require concerted efforts from the global invasion genomics community. 

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4. Immunoglobulin G N-glycan markers of accelerated biological aging during chronic HIV infection

   https://doi.org/10.1038/s41467-024-47279-4  

   Giron, Leila B; Liu, Qin; Adeniji, Opeyemi S; Yin, Xiangfan; Kannan, Toshitha; Ding, Jianyi; Lu, David Y; Langan, Susan; Zhang, Jinbing; Azevedo, Joao_L_L C; et al (December 2024, Nature Communications)

   Abstract People living with HIV (PLWH) experience increased vulnerability to premature aging and inflammation-associated comorbidities, even when HIV replication is suppressed by antiretroviral therapy (ART). However, the factors associated with this vulnerability remain uncertain. In the general population, alterations in theN-glycans on IgGs trigger inflammation and precede the onset of aging-associated diseases. Here, we investigate the IgGN-glycans in cross-sectional and longitudinal samples from 1214 women and men, living with and without HIV. PLWH exhibit an accelerated accumulation of pro-aging-associated glycan alterations and heightened expression of senescence-associated glycan-degrading enzymes compared to controls. These alterations correlate with elevated markers of inflammation and the severity of comorbidities, potentially preceding the development of such comorbidities. Mechanistically, HIV-specific antibodies glycoengineered with these alterations exhibit a reduced ability to elicit anti-HIV Fc-mediated immune activities. These findings hold potential for the development of biomarkers and tools to identify and prevent premature aging and comorbidities in PLWH. 

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5. Remodelling selection to optimise disease forecasts and policies

   https://doi.org/10.1088/1751-8121/ad280d  

   Gomes, M_Gabriela M; Blagborough, Andrew M; Langwig, Kate E; Ringwald, Beate (February 2024, Journal of Physics A: Mathematical and Theoretical)

   Abstract Mathematical models are increasingly adopted for setting disease prevention and control targets. As model-informed policies are implemented, however, the inaccuracies of some forecasts become apparent, for example overprediction of infection burdens and intervention impacts. Here, we attribute these discrepancies to methodological limitations in capturing the heterogeneities of real-world systems. The mechanisms underpinning risk factors of infection and their interactions determine individual propensities to acquire disease. These factors are potentially so numerous and complex that to attain a full mechanistic description is likely unfeasible. To contribute constructively to the development of health policies, model developers either leave factors out (reductionism) or adopt a broader but coarse description (holism). In our view, predictive capacity requires holistic descriptions of heterogeneity which are currently underutilised in infectious disease epidemiology, in comparison to other population disciplines, such as non-communicable disease epidemiology, demography, ecology and evolution. 

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