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  1. Article ; Online: Informing policy via dynamic models: Cholera in Haiti.

    Wheeler, Jesse / Rosengart, AnnaElaine / Jiang, Zhuoxun / Tan, Kevin / Treutle, Noah / Ionides, Edward L

    PLoS computational biology

    2024  Volume 20, Issue 4, Page(s) e1012032

    Abstract: Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. ... ...

    Abstract Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.
    MeSH term(s) Haiti/epidemiology ; Cholera/epidemiology ; Cholera/transmission ; Cholera/prevention & control ; Humans ; Computational Biology/methods ; Epidemics/statistics & numerical data ; Epidemics/prevention & control ; Epidemiological Models ; Health Policy ; Likelihood Functions ; Stochastic Processes ; Models, Statistical
    Language English
    Publishing date 2024-04-29
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1012032
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.

    Omelchenko, Alisa A / Siwek, Jane C / Chhibbar, Prabal / Arshad, Sanya / Nazarali, Iliyan / Nazarali, Kiran / Rosengart, AnnaElaine / Rahimikollu, Javad / Tilstra, Jeremy / Shlomchik, Mark J / Koes, David R / Joglekar, Alok V / Das, Jishnu

    bioRxiv : the preprint server for biology

    2024  

    Abstract: The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide- ... ...

    Abstract The explosion of sequence data has allowed the rapid growth of protein language models (pLMs). pLMs have now been employed in many frameworks including variant-effect and peptide-specificity prediction. Traditionally, for protein-protein or peptide-protein interactions (PPIs), corresponding sequences are either co-embedded followed by post-hoc integration or the sequences are concatenated prior to embedding. Interestingly, no method utilizes a language representation of the interaction itself. We developed an interaction LM (iLM), which uses a novel language to represent interactions between protein/peptide sequences. S liding W indow In teraction G rammar (SWING) leverages differences in amino acid properties to generate an interaction vocabulary. This vocabulary is the input into a LM followed by a supervised prediction step where the LM's representations are used as features. SWING was first applied to predicting peptide:MHC (pMHC) interactions. SWING was not only successful at generating Class I and Class II models that have comparable prediction to state-of-the-art approaches, but the unique Mixed Class model was also successful at jointly predicting both classes. Further, the SWING model trained only on Class I alleles was predictive for Class II, a complex prediction task not attempted by any existing approach. For de novo data, using only Class I or Class II data, SWING also accurately predicted Class II pMHC interactions in murine models of SLE (MRL/lpr model) and T1D (NOD model), that were validated experimentally. To further evaluate SWING's generalizability, we tested its ability to predict the disruption of specific protein-protein interactions by missense mutations. Although modern methods like AlphaMissense and ESM1b can predict interfaces and variant effects/pathogenicity per mutation, they are unable to predict interaction-specific disruptions. SWING was successful at accurately predicting the impact of both Mendelian mutations and population variants on PPIs. This is the first generalizable approach that can accurately predict interaction-specific disruptions by missense mutations with only sequence information. Overall, SWING is a first-in-class generalizable zero-shot iLM that learns the language of PPIs.
    Language English
    Publishing date 2024-05-04
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.05.01.592062
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

    Rahimikollu, Javad / Xiao, Hanxi / Rosengart, AnnaElaine / Rosen, Aaron B I / Tabib, Tracy / Zdinak, Paul M / He, Kun / Bing, Xin / Bunea, Florentina / Wegkamp, Marten / Poholek, Amanda C / Joglekar, Alok V / Lafyatis, Robert A / Das, Jishnu

    Nature methods

    2024  Volume 21, Issue 5, Page(s) 835–845

    Abstract: Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets ... ...

    Abstract Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.
    MeSH term(s) Machine Learning ; Humans ; Computational Biology/methods ; Animals ; Single-Cell Analysis/methods ; Algorithms
    Language English
    Publishing date 2024-02-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2169522-2
    ISSN 1548-7105 ; 1548-7091
    ISSN (online) 1548-7105
    ISSN 1548-7091
    DOI 10.1038/s41592-024-02175-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Association between conventional or blue-light-filtering intraocular lenses and survival in bilateral cataract surgery patients.

    Griepentrog, John E / Zhang, Xianghong / Marroquin, Oscar C / Garver, Michael B / Rosengart, AnnaElaine L / Chung-Chou Chang, Joyce / Esfandiari, Hamed / Loewen, Nils A / Rosengart, Matthew R

    iScience

    2020  Volume 24, Issue 1, Page(s) 102009

    Abstract: Circadian rhythms regulate adaptive alterations in mammalian physiology and are maximally entrained by the short wavelength blue spectrum; cataracts block the transmission of light, particularly blue light. Cataract surgery is performed with two types of ...

    Abstract Circadian rhythms regulate adaptive alterations in mammalian physiology and are maximally entrained by the short wavelength blue spectrum; cataracts block the transmission of light, particularly blue light. Cataract surgery is performed with two types of intraocular lenses (IOL): (1) conventional IOL that transmit the entire visible spectrum and (2) blue-light-filtering (BF) IOL that block the short wavelength blue spectrum. We hypothesized that the transmission properties of IOL are associated with long-term survival. This retrospective cohort study of a 15-hospital healthcare system identified 9,108 participants who underwent bilateral cataract surgery; 3,087 were implanted with conventional IOL and 6,021 received BF-IOL. Multivariable Cox proportional hazards models that included several
    Language English
    Publishing date 2020-12-29
    Publishing country United States
    Document type Journal Article
    ISSN 2589-0042
    ISSN (online) 2589-0042
    DOI 10.1016/j.isci.2020.102009
    Database MEDical Literature Analysis and Retrieval System OnLINE

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