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  1. Article: Crosstalkr: An open-source R package to facilitate drug target identification.

    Weaver, Davis T / Scott, Jacob G

    bioRxiv : the preprint server for biology

    2023  

    Abstract: In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known ... ...

    Abstract In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known protein and every other known protein. Integration of these interactomic data into bioinformatic pipelines may increase the translational potential of discoveries made through analysis of multi-omic datasets. Crosstalkr provides a unified toolkit for drug target and disease subnetwork identification, two of the most common uses of protein protein interaction networks. First, crosstalkr enables users to download and leverage high-quality protein-protein interaction networks from online repositories. Users can then filter these large networks into manageable subnetworks using a variety of methods. For example, network filtration can be done using random walks with restarts, starting at the user-provided seed proteins. Affinity scores from a given random walk with restarts are compared to a bootstrapped null distribution to assess statistical significance. Random walks are implemented using sparse matrix multiplication to facilitate fast execution. Next, users can perform in-silico repression experiments to assess the relative importance of nodes in their network. At this step, users can supply protein or gene expression data to make node rankings more meaningful. The default behavior evaluates the human interactome. However, users can evaluate more than 1000 non-human protein-protein interaction networks as a result of integration with StringDB. It is a free, open-source R package designed to allow users to integrate functional analysis using the protein-protein interaction network into existing bioinformatic pipelines. A beta version of crosstalkr available on CRAN (https://cran.rstudio.com/web/packages/crosstalkr/index.html).
    Language English
    Publishing date 2023-03-10
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.03.07.531526
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Viruses, cancers, and evolutionary biology in the clinic: a commentary on Leeks et al. 2023.

    Ågren, J Arvid / Scott, Jacob G

    Journal of evolutionary biology

    2023  Volume 36, Issue 11, Page(s) 1587–1589

    MeSH term(s) Onions ; Viruses ; Biological Evolution ; Neoplasms/genetics ; Biology
    Language English
    Publishing date 2023-11-17
    Publishing country Switzerland
    Document type Journal Article ; Comment
    ZDB-ID 1465318-7
    ISSN 1420-9101 ; 1010-061X
    ISSN (online) 1420-9101
    ISSN 1010-061X
    DOI 10.1111/jeb.14232
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: A low-footprint, fluorescence-based bacterial time-kill assay for estimating dose-dependent cell death dynamics.

    King, Eshan S / Stacy, Anna E / Scott, Jacob G

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Dose-response curves that describe the relationship between antibiotic dose and growth rate in bacteria are commonly measured with optical density (OD) based assays. While being simple and high-throughput, any dose-dependent cell death dynamics are ... ...

    Abstract Dose-response curves that describe the relationship between antibiotic dose and growth rate in bacteria are commonly measured with optical density (OD) based assays. While being simple and high-throughput, any dose-dependent cell death dynamics are obscured, as OD assays in batch culture can only quantify a positive net change in cells. Time-kill experiments can be used to quantify cell death rates, but current techniques are extremely resource-intensive and may be biased by residual drug carried over into the quantification assay. Here, we report a novel, fluorescence-based time-kill assay leveraging resazurin as a viable cell count indicator. Our method improves upon previous techniques by greatly reducing the material cost and being robust to residual drug carry-over. We demonstrate our technique by quantifying a dose-response curve in
    Language English
    Publishing date 2024-03-19
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.08.584154
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A Phenome-Wide Association Study and the Discovery of a New Clinical Spectrum of Hereditary Cancer Genes.

    Yang, Kailin / Scott, Jacob G

    JAMA oncology

    2022  Volume 8, Issue 6, Page(s) 827–828

    MeSH term(s) Genetic Association Studies ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Neoplasms/genetics ; Phenotype ; Polymorphism, Single Nucleotide
    Language English
    Publishing date 2022-04-22
    Publishing country United States
    Document type Journal Article
    ISSN 2374-2445
    ISSN (online) 2374-2445
    DOI 10.1001/jamaoncol.2022.0361
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches.

    Durmaz, Arda / Scott, Jacob G

    Evolutionary bioinformatics online

    2022  Volume 18, Page(s) 11769343221123050

    Abstract: Background: Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, ... ...

    Abstract Background: Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference.
    Methods: We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6
    Results: Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.
    Language English
    Publishing date 2022-09-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2227610-5
    ISSN 1176-9343
    ISSN 1176-9343
    DOI 10.1177/11769343221123050
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.

    Weaver, Davis T / King, Eshan S / Maltas, Jeff / Scott, Jacob G

    Proceedings of the National Academy of Sciences of the United States of America

    2024  Volume 121, Issue 16, Page(s) e2303165121

    Abstract: Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and ... ...

    Abstract Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated
    MeSH term(s) Learning ; Reinforcement, Psychology ; Drug Resistance, Microbial ; Bicycling ; Escherichia coli/genetics ; Anti-Infective Agents
    Chemical Substances Anti-Infective Agents
    Language English
    Publishing date 2024-04-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2303165121
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Inferring density-dependent population dynamics mechanisms through rate disambiguation for logistic birth-death processes.

    Huynh, Linh / Scott, Jacob G / Thomas, Peter J

    Journal of mathematical biology

    2023  Volume 86, Issue 4, Page(s) 50

    Abstract: Density dependence is important in the ecology and evolution of microbial and cancer cells. Typically, we can only measure net growth rates, but the underlying density-dependent mechanisms that give rise to the observed dynamics can manifest in birth ... ...

    Abstract Density dependence is important in the ecology and evolution of microbial and cancer cells. Typically, we can only measure net growth rates, but the underlying density-dependent mechanisms that give rise to the observed dynamics can manifest in birth processes, death processes, or both. Therefore, we utilize the mean and variance of cell number fluctuations to separately identify birth and death rates from time series that follow stochastic birth-death processes with logistic growth. Our nonparametric method provides a novel perspective on stochastic parameter identifiability, which we validate by analyzing the accuracy in terms of the discretization bin size. We apply our method to the scenario where a homogeneous cell population goes through three stages: (1) grows naturally to its carrying capacity, (2) is treated with a drug that reduces its carrying capacity, and (3) overcomes the drug effect to restore its original carrying capacity. In each stage, we disambiguate whether the dynamics occur through the birth process, death process, or some combination of the two, which contributes to understanding drug resistance mechanisms. In the case of limited sample sizes, we provide an alternative method based on maximum likelihood and solve a constrained nonlinear optimization problem to identify the most likely density dependence parameter for a given cell number time series. Our methods can be applied to other biological systems at different scales to disambiguate density-dependent mechanisms underlying the same net growth rate.
    MeSH term(s) Cell Count ; Ecology ; Population Dynamics ; Sample Size ; Time Factors
    Language English
    Publishing date 2023-03-03
    Publishing country Germany
    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 187101-8
    ISSN 1432-1416 ; 0303-6812
    ISSN (online) 1432-1416
    ISSN 0303-6812
    DOI 10.1007/s00285-023-01877-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Translation of Precision Medicine Research Into Biomarker-Informed Care in Radiation Oncology.

    Scarborough, Jessica A / Scott, Jacob G

    Seminars in radiation oncology

    2021  Volume 32, Issue 1, Page(s) 42–53

    Abstract: The reach of personalized medicine in radiation oncology has expanded greatly over the past few decades as technical precision has improved the delivery of radiation to each patient's unique anatomy. Yet, the consideration of biological heterogeneity ... ...

    Abstract The reach of personalized medicine in radiation oncology has expanded greatly over the past few decades as technical precision has improved the delivery of radiation to each patient's unique anatomy. Yet, the consideration of biological heterogeneity between patients has largely not been translated to clinical care. There are innumerable promising advancements in the discovery and validation of biomarkers, which could be used to alter radiation therapy directly or indirectly. Directly, biomarker-informed care may alter treatment dose or identify patients who would benefit most from radiation therapy and who could safely avoid more aggressive care. Indirectly, a variety of biomarkers could assist with choosing the best radiosensitizing chemotherapies. The translation of these advancements into clinical practice will bring radiation oncology even further into the era of precision medicine, treating patients according to their unique anatomical and biological differences.
    MeSH term(s) Biomarkers ; Humans ; Neoplasms/radiotherapy ; Precision Medicine ; Radiation Oncology
    Chemical Substances Biomarkers
    Language English
    Publishing date 2021-11-18
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Review
    ZDB-ID 1146999-7
    ISSN 1532-9461 ; 1053-4296
    ISSN (online) 1532-9461
    ISSN 1053-4296
    DOI 10.1016/j.semradonc.2021.09.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Diverse mutant selection windows shape spatial heterogeneity in evolving populations.

    King, Eshan S / Tadele, Dagim S / Pierce, Beck / Hinczewski, Michael / Scott, Jacob G

    PLoS computational biology

    2024  Volume 20, Issue 2, Page(s) e1011878

    Abstract: Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model ...

    Abstract Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes in both cancer and infectious diseases. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more fully reflect the selection of drug resistant genotypes. Furthermore, using synthetic data and empirical dose-response data in cancer, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. By simulating a tumor treated with cyclic drug therapy, we find that mutant selection windows introduced by drug diffusion promote the proliferation of drug resistant cells. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.
    MeSH term(s) Humans ; Mutation ; Genotype ; Neoplasms/drug therapy ; Neoplasms/genetics ; Selection, Genetic
    Language English
    Publishing date 2024-02-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1011878
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.

    Weaver, Davis T / King, Eshan S / Maltas, Jeff / Scott, Jacob G

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and ... ...

    Abstract Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known
    Language English
    Publishing date 2023-11-16
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.01.12.523765
    Database MEDical Literature Analysis and Retrieval System OnLINE

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