ReviewPatterns (New York, N.Y.)2021
A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks.
Review in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Why family medicine requires a paradigm shift from linear guidelines to complexity science.Family medicine and community health · 2026Article
- Excitatory neurons and astrocytes-specific dysregulation and aberrant interactions are vulnerable to FCDI as suggested by single-cell spatial transcriptomics.Clinical and translational medicine · 2026Article
- Energy landscapes and synergetic state transitions in frustrated Stuart-Landau oscillator networks: a homotopy continuation study.Frontiers in network physiology · 2026Article
- A novel hybrid deep learning and chaotic dynamics approach for thyroid cancer classification.Scientific reports · 2025Article
- Optimal control of multiple myeloma assuming drug resistance and off-target effects.PLoS computational biology · 2025Article
- Metabolic Objectives and Trade-Offs: Inference and Applications.Metabolites · 2025Review
- Cancer complexity: why we need a novel cancer research strategy.Frontiers in oncology · 2025Article
- Targeting the role played by microbiota and adaptive immunity within autoimmune disease complexity.Frontiers in immunology · 2025Article
- Research trends, hotspots and future directions of tertiary lymphoid structures in cancer: a comprehensive informatics analysis and visualization study.Discover oncology · 2024Article
- A method for in silico exploration of potential glioblastoma multiforme attractors using single-cell RNA sequencing.Scientific reports · 2024Article
- Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.International journal of molecular sciences · 2024Article
- A logic-incorporated gene regulatory network deciphers principles in cell fate decisions.eLife · 2024Article
- Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy.Frontiers in oncology · 2024Review
- Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory.Advances in neurobiology · 2024Review
- Emerging cancer therapies: targeting physiological networks and cellular bioelectrical differences with non-thermal systemic electromagnetic fields in the human body - a comprehensive review.Frontiers in network physiology · 2024Review
- Mitochondria Transfer from Mesenchymal Stem Cells Confers Chemoresistance to Glioblastoma Stem Cells through Metabolic Rewiring.Cancer research communications · 2023Article
- Cell Adaptive Fitness and Cancer Evolutionary Dynamics.Cancer informatics · 2023Article
- Evidence of self-organized criticality in time series by the horizontal visibility graph approach.Scientific reports · 2022Article
- Executable models of immune signaling pathways in HIV-associated atherosclerosis.NPJ systems biology and applications · 2022Article
- ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin.NPJ breast cancer · 2022Article
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Abstract
Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.