Topological data analysis to model the shape of immune responses during co-infections

Sasaki, Karin; Bruder, Dunja; Hernandez‐Vargas, Esteban A.

doi:10.1016/j.cnsns.2020.105228

Cited by 12 publications

(3 citation statements)

References 25 publications

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“…Among the parameters that can be modified are the metric space, lens, clustering algorithm, number of intervals and percentage overlap. In a previos work, Sasaki et al [40] studied with TDA the shape of the immune response during co-infections, in this work the mapper algorithm and each of the listed parameters are described in detail; it is recommended to refer to that work to obtain a broader overview of this analysis tool.…”

Section: Methodologiesmentioning

confidence: 99%

Quantifying antibody dynamics of severe and non-severe patients with COVID-19

Ordonez-Jimenez¹,

Blanco-Rodríguez²,

Almocera

et al. 2022

Preprint

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COVID-19 pandemic is a major public health threat with unanswered questions regarding the role of the immune system in the severity level of the disease. In this paper, based on antibody kinetic data of patients with different disease severity, topological data analysis highlights clear differences in the shape of antibody dynamics between three groups of patients, which were non-severe, severe, and one intermediate case of severity. Subsequently, different mathematical models were developed to quantify the dynamics between the different severity groups. The best model was the one with the lowest media value of Akaike Information Criterion for all groups of patients. Although it has been reported high IgG level in severe patients, our findings suggest that IgG antibodies in severe patients may be less effective than non-severe patients due to early B cell production and early activation of the seroconversion process from IgM to IgG antibody.

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Section: Methodologiesmentioning

confidence: 99%

Quantifying antibody dynamics of severe and non-severe patients with COVID-19

Ordonez-Jimenez¹,

Blanco-Rodríguez²,

Almocera

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…21 TDA has been extensively used in biomedical research. [22][23][24][25] This is due to the fact that we now have massive amounts of data, but our ability to analyze and visualize the data still lags behind. By using TDA methods, researches have found a way to understand the structures and the underlying shapes of high-dimensional datasets.…”

Section: Tda and The Mapper Algorithmmentioning

confidence: 99%

Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

Afrasiabi

Dehghanpoor

Haspel

2021

Preprint

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Investigating the conformational space of proteins is essential in order to associate their structures with their fundamental functions. Nonetheless, it is a challenging task, both experimentally and computationally. Because of the transient nature of these conformational changes and the fact that they are impermanent, empirical methods have fallen short to capture them. In silico methods, on the other hand, have shown great promise in exploring these conformational pathways. In this article, we provide an extensive evaluation of our previously introduced, robotics inspired conformational search algorithm (RRT* with Monte Carlo). We then identify what intermediate conformations appear the most in our generated conformational pathways using TDA Mapper, a topological data analysis algorithm, and examine how close these intermediate conformations are to existing experimental data.

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“…TDA and the Mapper Algorithm: Topological Data Analysis (TDA) has been employed in recent years to analyze complex high-dimensional datasets and extract valuable information and insights [17]. TDA has been extensively used in biomedical research [18,19]. By using TDA methods, researchers have found a way to understand the structures and the underlying shapes of high-dimensional datasets.…”

Section: Overview Of Rrt* Search and Tdamentioning

confidence: 99%

Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

Dehghanpoor¹,

Afrasiabi²,

Haspel³

EPiC Series in Computing

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An essential step to understanding how different functionalities of proteins work is to explore their conformational space. However, because of the fleeting nature of conforma- tional changes in proteins, investigating protein conformational spaces is a challenging task to do experimentally. Nonetheless, computational methods have shown to be practical to explore these conformational pathways. In this work, we use Topological Data Analysis (TDA) methods to evaluate our previously introduced algorithm called RRTMC, that uses a combination of Rapidly-exploring Random Trees algorithm and Monte Carlo criteria to explore these pathways. TDA is used to identify the intermediate conformations that are generated the most by RRTMC and examine how close they are to existing known inter- mediate conformations. We concluded that the intermediate conformations generated by RRTMC are close to existing experimental data and that TDA can be a helpful tool to analyze protein conformation sampling methods.

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Topological data analysis to model the shape of immune responses during co-infections

Cited by 12 publications

References 25 publications

Quantifying antibody dynamics of severe and non-severe patients with COVID-19

Quantifying antibody dynamics of severe and non-severe patients with COVID-19

Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

Using Topological Data Analysis and RRT to Investigate Protein Conformational Spaces

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