dcor: Distance correlation and energy statistics in Python

Ramos-Carreño, Carlos; Torrecilla, José L.

doi:10.1016/j.softx.2023.101326

Cited by 9 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A zero value indicates total independence, while higher values indicate greater dependence (Székely et al, 2007). The dCor was calculated using the distance correlation function implemented in the dCor 0.5 package (Ramos-Carreño & Torrecilla, 2023), and hierarchical clustering analysis was performed using the clustermap function and the average method implemented in the seaborn 0.11.2 package (Waskom, 2021).…”

Section: Methodsmentioning

confidence: 99%

Transcriptomic signatures and network-based methods uncover new Senescent Cell Anti-Apoptotic Pathways and Senolytics

Olascoaga,

Konigsberg,

Espinal-Enríquez

et al. 2024

Preprint

View full text Add to dashboard Cite

Cellular senescence is an irreversible cell cycle arrest caused by various stressors that damage cells. Over time, senescent cells accumulate and contribute to the progression of multiple age-related degenerative diseases. It is believed that these cells accumulate partly due to their ability to evade programmed cell death through the development and activation of survival and anti-apoptotic resistance mechanisms; however, many aspects of how these survival mechanisms develop and activate are still unknown. By analyzing transcriptomic signature profiles generated by the LINCS L1000 project and using network-based methods, we identified various genes that could represent new senescence-related survival mechanisms. Additionally, employing the same methodology, we identified over 600 molecules with potential senolytic activity. Experimental validation of our computational findings confirmed the senolytic activity of Fluorouracil, whose activity would be mediated by a multi-target mechanism, revealing that its targets AURKA, EGFR, IRS1, SMAD4, and KRAS are new senescence-associated survival and anti-apoptotic resistance pathways. The development of these pathways could depend on the stimulus that induces cellular senescence. The SCAPs development and activation mechanisms proposed in this work offer new insights into how senescent cells survive. Identifying new anti-apoptotic resistance targets and drugs with potential senolytic activity paves the way for developing new pharmacological therapies to eliminate senescent cells selectively.Graphical Abstract

show abstract

Section: Methodsmentioning

confidence: 99%

Transcriptomic signatures and network-based methods uncover new Senescent Cell Anti-Apoptotic Pathways and Senolytics

Olascoaga,

Konigsberg,

Espinal-Enríquez

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Improved distance correlation estimation

Monroy-Castillo,

Jácome,

Cao

2025

Appl Intell

View full text Add to dashboard Cite

Enhancing the Vietoris–Rips simplicial complex for topological data analysis: applications in cancer gene expression datasets

Mashatola,

Kader,

Abdulla

et al. 2024

Int J Data Sci Anal

View full text Add to dashboard Cite

The aim of this study is to enhance the extraction of informative features from complex data through the application of topological data analysis (TDA) using novel topological overlapping measures. Topological data analysis has emerged as a promising methodology for extracting meaningful insights from complex datasets. Existing approaches in TDA often involve extrapolating data points using distance correlation measures, which subsequently constrain downstream predictive tasks. Our objective is to improve the construction of the Vietoris–Rips simplicial complex by introducing topological overlapping measures. These measures take into account the interplay of direct connection strengths and shared neighbours, leading to the identification of persistent topological features. We propose the utilisation of topological overlapping measures to optimise the construction of the Vietoris–Rips simplicial complex, offering a more refined representation of complex data structures. The application of topological overlapping measures results in the identification of plentiful persistent topological features. This enhancement contributes to an improvement of up to 20% in cancer phenotype prediction across different cancer types. Our study demonstrates the effectiveness of utilising topological overlapping measures in optimising the construction of the Vietoris–Rips simplicial complex. The identified persistent topological features significantly enhance the predictive accuracy of cancer phenotypes. This novel approach has the potential to advance the field of topological data analysis and improve our understanding of complex data structures, particularly in the context of cancer research and predictive modelling. Further exploration and application of these measures may yield valuable insights in various domains dealing with intricate datasets.

show abstract

dcor: Distance correlation and energy statistics in Python

Cited by 9 publications

References 39 publications

Transcriptomic signatures and network-based methods uncover new Senescent Cell Anti-Apoptotic Pathways and Senolytics

Transcriptomic signatures and network-based methods uncover new Senescent Cell Anti-Apoptotic Pathways and Senolytics

Improved distance correlation estimation

Enhancing the Vietoris–Rips simplicial complex for topological data analysis: applications in cancer gene expression datasets

Contact Info

Product

Resources

About