Graphical models for inference: A model comparison approach for analyzing bacterial conjugation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916424468922368 |
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| author | Kendal-Freedman, Nat Meleshko, Joseph Victor Fiorillo Yip, Aaron Ingalls, Brian |
| author_facet | Kendal-Freedman, Nat Meleshko, Joseph Victor Fiorillo Yip, Aaron Ingalls, Brian |
| contents | We present a proof-of-concept of a model comparison approach for analyzing spatio-temporal observations of interacting populations. Our model variants are a collection of structurally similar Bayesian networks. Their distinct Noisy-Or conditional probability distributions describe interactions within the population, with each distribution corresponding to a specific mechanism of interaction. To determine which distributions most accurately represent the underlying mechanisms, we examine the accuracy of each Bayesian network with respect to observational data. We implement such a system for observations of bacterial populations engaged in conjugation, a type of horizontal gene transfer that allows microbes to share genetic material with nearby cells through physical contact. Evaluating cell-specific factors that affect conjugation is generally difficult because of the stochastic nature of the process. Our approach provides a new method for gaining insight into this process. We compare eight model variations for each of three experimental trials and rank them using two different metrics |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03814 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graphical models for inference: A model comparison approach for analyzing bacterial conjugation Kendal-Freedman, Nat Meleshko, Joseph Victor Fiorillo Yip, Aaron Ingalls, Brian Methodology Cell Behavior We present a proof-of-concept of a model comparison approach for analyzing spatio-temporal observations of interacting populations. Our model variants are a collection of structurally similar Bayesian networks. Their distinct Noisy-Or conditional probability distributions describe interactions within the population, with each distribution corresponding to a specific mechanism of interaction. To determine which distributions most accurately represent the underlying mechanisms, we examine the accuracy of each Bayesian network with respect to observational data. We implement such a system for observations of bacterial populations engaged in conjugation, a type of horizontal gene transfer that allows microbes to share genetic material with nearby cells through physical contact. Evaluating cell-specific factors that affect conjugation is generally difficult because of the stochastic nature of the process. Our approach provides a new method for gaining insight into this process. We compare eight model variations for each of three experimental trials and rank them using two different metrics |
| title | Graphical models for inference: A model comparison approach for analyzing bacterial conjugation |
| topic | Methodology Cell Behavior |
| url | https://arxiv.org/abs/2410.03814 |