A computational model for cancer growth by using complex networks

Galvão, Viviane; Miranda, José García Vivas

doi:10.1016/j.physa.2008.05.010

Cited by 2 publications

(1 citation statement)

References 46 publications

(71 reference statements)

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“…Common network clustering algorithms (e.g., Bayesian clustering, K-means, spectral clustering, agglomerative hierarchical clustering) and pairwise interactions’ information theoretic measures (e.g., entropy scores, mutual information) are then used to infer statistically correlated network structures. 82 , 132 For instance, cancer networks can be graph partitioned into smaller sub-networks via hierarchical clustering and dimensionality reduction techniques. 133 Several measures can be used to assess the similarity of two time series gene expression datasets, both model-free, such as Euclidean distance, correlation and lag-correlation, and model-based methods, such as the Kullback-Leibler distance.…”

Section: Network Science and Pattern Inferencementioning

confidence: 99%

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Uthamacumaran

2021

Patterns

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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. ll

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Section: Network Science and Pattern Inferencementioning

confidence: 99%