Representation of texture structures with topological data analysis for stage IA lung adenocarcinoma in three-dimensional thoracic CT images

2021 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Masahiko Kusumoto ◽  
Hironobu Ohamatsu ◽  
Keiju Aokage ◽  
...  
Keyword(s):  
Data Analysis ◽  
Lung Adenocarcinoma ◽  
Three Dimensional ◽  
Ct Images ◽  
Topological Data Analysis ◽  
Thoracic Ct ◽  
Stage Ia ◽  
Topological Data

scholarly journals A COVID-19 Drug Repurposing Strategy Through Quantitative Homological Similarities by using a Topological Data Analysis Based Formalism

2020 ◽  
Author(s):  
Raul Pérez-Moraga ◽  
Jaume Forés-Martos ◽  
Beatriz Suay ◽  
Jean-Louis Duval ◽  
Antonio Falcó ◽  
...  
Keyword(s):  
Data Analysis ◽  
Algebraic Topology ◽  
Protein Structures ◽  
Three Dimensional ◽  
Drug Repurposing ◽  
Topological Data Analysis ◽  
Drug Identification ◽  
Global Pandemic ◽  
Candidate Drug ◽  
Topological Data

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 65 million cases and one point five million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We created a novel computational pipeline aimed to speed up the process of repurposable candidate drug identification. Compared with current drug repurposing methodologies, our strategy is centered on filtering the best candidate among all selected targets focused on the introduction of a mathematical formalism motivated by recent advances in the fields of algebraic topology and topological data analysis (TDA). This formalism allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib among others. To our knowledge, it is the first time that a TDA based strategy has been used to compare a massive amount of protein structures with the final objective of performing drug repurposing

scholarly journals A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 488
Author(s):  
Raul Pérez-Moraga ◽  
Jaume Forés-Martos ◽  
Beatriz Suay-García ◽  
Jean-Louis Duval ◽  
Antonio Falcó ◽  
...  
Keyword(s):  
Data Analysis ◽  
Protein Structures ◽  
Three Dimensional ◽  
Drug Repurposing ◽  
Viral Proteins ◽  
Topological Data Analysis ◽  
Global Pandemic ◽  
Massive Number ◽  
First Time ◽  
Topological Data

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.

scholarly journals Evolution of local motifs and topological proximity in self-assembled quasi-crystalline phases

2020 ◽  
Vol 476 (2241) ◽  
pp. 20200170
Author(s):  
Martin Cramer Pedersen ◽  
Vanessa Robins ◽  
Kell Mortensen ◽  
Jacob J. K. Kirkensgaard
Keyword(s):  
Data Analysis ◽  
Self Assembly ◽  
Persistent Homology ◽  
Three Dimensional ◽  
Topological Data Analysis ◽  
Structure Identification ◽  
Colloidal System ◽  
Topological Proximity ◽  
Topological Data ◽  
Persistence Diagrams

Using methods from the field of topological data analysis, we investigate the self-assembly and emergence of three-dimensional quasi-crystalline structures in a single-component colloidal system. Combining molecular dynamics and persistent homology, we analyse the time evolution of persistence diagrams and particular local structural motifs. Our analysis reveals the formation and dissipation of specific particle constellations in these trajectories, and shows that the persistence diagrams are sensitive to nucleation and convergence to a final structure. Identification of local motifs allows quantification of the similarities between the final structures in a topological sense. This analysis reveals a continuous variation with density between crystalline clathrate, quasi-crystalline, and disordered phases quantified by ‘topological proximity’, a visualization of the Wasserstein distances between persistence diagrams. From a topological perspective, there is a subtle, but direct connection between quasi-crystalline, crystalline and disordered states. Our results demonstrate that topological data analysis provides detailed insights into molecular self-assembly.

scholarly journals Topological data analysis reveals principles of chromosome structure throughout cellular differentiation

2019 ◽  
Author(s):  
Natalie Sauerwald ◽  
Yihang Shen ◽  
Carl Kingsford
Keyword(s):  
Data Analysis ◽  
Cell Lines ◽  
Cellular Differentiation ◽  
Structural Changes ◽  
Chromosome Structure ◽  
Three Dimensional ◽  
Topological Data Analysis ◽  
Data Sets ◽  
Chromosomal Architecture ◽  
Topological Data

AbstractThree-dimensional chromosome structure has a significant influence in many diverse genomic processes and has recently been shown to relate to cellular differentiation. Many methods for describing the chromosomal architecture focus on specific substructures such as topologically-associating domains (TADs) or compartments, but we are still missing a global view of all geometric features of chromosomes. Topological data analysis (TDA) is a mathematically well-founded set of methods to derive robust information about the structure and topology of data sets, making it well-suited to better understand the key features of chromosome structure. By applying TDA to the study of chromosome structure through differentiation across three cell lines, we provide insight into principles of chromosome folding generally, and observe structural changes across lineages. We identify both global and local differences in chromosome topology through differentiation, identifying trends consistent across human cell lines.AvailabilityScripts to reproduce the results from this study can be found at https://github.com/Kingsford-Group/[email protected]

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