Network analysis with biological data of cancer patients: A scoping review

Kosvyra, Alexandra; Ntzioni, E.; Chouvarda, Ioanna

doi:10.1016/j.jbi.2021.103873

Cited by 6 publications

(4 citation statements)

References 132 publications

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“…El estudio se realizó mediante adopta una metodología de revisión documental comprensiva (scoping review), diseñada para recopilar, analizar e interpretar de manera exhaustiva la literatura relevante sobre los aportes de la teoría de grafos a la comprensión de las dinámicas sociales, los principales desafíos de su aplicación y líneas promisorias para su desarrollo (Khalil et al, 2023;Kyrimi et al, 2021;Linares Giraldo et al, 2023;Vieira et al, 2023;Zimmern, 2020). Este enfoque metodológico se estructuró en varias fases subsecuentes, detalladas a continuación, así como en el estudio exhaustivo de antecedentes investigativos claves (Kosvyra et al, 2021;Priego Morales, 2024). La principal ventaja de este tipo de estudios es que permite un mejor acercamiento a aspectos primordiales de las categorías en estudios y, más que presentar análisis cientométricos, ofrece importantes avances en cuanto a contenidos.…”

Section: Metodologíaunclassified

Contribution of graph theory to the understanding of social dynamics

Gamboa

2023

AWARI

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This review article examines how graph theory has contributed significantly to understanding social dynamics by discussing its applicability to social network analysis. A predominantly qualitative study was carried out, with a scoping review design aimed at understanding how this set of tools facilitates studying human interactions and the structure of social groups. The main results highlighted key concepts such as centrality, clusters, connectivity, and network resilience, as well as their applications to analyzing social phenomena such as information diffusion, opinion formation, disease spread, and social cohesion. In addition, the methodological challenges and limitations of graph theory were examined, to propose future directions for interdisciplinary research that delve into the interaction between mathematics and social sciences, especially Social Psychology applied to collective behavior and the social network analysis.

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Section: Metodologíaunclassified

Contribution of graph theory to the understanding of social dynamics

Gamboa

2023

AWARI

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“…Network analysis is typically understood as a collection of methodologies and heuristics that allow us to understand the relationships among entities within complex systems. Network analysis finds application in human fields such as sociology [1,2], economics [3][4][5], biology [6,7], medicine [8,9], and many others. For example, in sociology, network analysis can identify online communities [10,11] and social interactions [12] or measure social influence within a friendship network.…”

Section: Introductionmentioning

confidence: 99%

Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem

Cavallaro,

Crespi,

Cutello

et al. 2024

Algorithms

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This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of a colony of memory-enhanced ants (ME-ANTS) which, starting from a given position, must collaboratively discover the optimal path to the exit point within a grid network. The colony can be divided into groups of different sizes and its objectives are maximizing the number of ants that exit the grid while minimizing path costs. Three distinct analyses were conducted: an overall analysis assessing colony performance across different-sized groups, a group analysis examining the performance of each partitioned group, and a pheromone distribution analysis discerning correlations between temporal pheromone distribution and ant navigation. From the results, a dynamic correlation emerged between the degree of colony partitioning and solution quality within the ACO algorithm framework.

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“…Network analysis in cancer genomics has begun to be used in the last five years, with strong growth in the last two years [6]. The field of "functional genomics" is concerned with the study of this complexity, and it is still in its infancy [7].…”

Section: Introductionmentioning

confidence: 99%

Polygenic and Network-based studies in risk identification and demystification of cancer

Hadi

Ayoub

Bachir

et al. 2022

Expert Review of Molecular Diagnostics

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Introduction: Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. Areas Covered:The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.Expert Opinion: In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity. Article highlights:• Diseases were originally thought to be the result of a single gene mutation, but advances in DNA sequencing have proven otherwise.• Methods for classifying genetic variants are evolving, and they all show that genetic variants need to be studied in a more robust manner.• Complex diseases occur when the right environmental factors and SNPs exist. The latter are accumulating without understanding their significance, requiring integration into multiomic studies.• Gene networks are dominated by universal laws, proving they are credible to consider as the new "units of study" instead of single genes.• Network and polygenic studies allow for more accurate prediction and prognosis of cancer risk, treatment-useful cancer subtyping, and the discovery of interesting cancer therapies.• Comprehensive network studies integrating all types of data (e.g. transcriptomics, blood serum omics, and environmental agents) are the future of medical care.

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Network analysis with biological data of cancer patients: A scoping review

Cited by 6 publications

References 132 publications

Contribution of graph theory to the understanding of social dynamics

Contribution of graph theory to the understanding of social dynamics

Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem

Polygenic and Network-based studies in risk identification and demystification of cancer

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