Research interest in identifying candidate genes/proteins as novel biomarkers for early detection, diagnosis, prognosis, or drug response continues to grow in the post-genome era. Identification of these genes and locus, while extremely valuable, is only the first step to the development of molecular profiling solutions for complex diseases, such as cancers, diabetes, and Alzheimer’s disease.
Network Biomarkers: From a view of network biology, the genes associated with a complex disease never function alone, but work together in a complex network. The concept of network biomarkers/signatures has already been proposed for candidate biomarker discovery by integrating disease susceptibility genes, gene expressions, and gene/protein networks together.
Complex Networks: Traditional network analyses often fail to find patterns in the ranked or clustered adjacency matrix of a complex network. Complex networks are often characterized by small-world and scale-free properties, which suggest that molecular entities may not have “absolute ranks” or “clear cluster boundary” among them. Could there be some emerging properties at systems-level for us to discover in complex molecular networks?
Dynamical Biomarkers: It has been found that both biological shapes (e.g. brain structures) and physiological signals (e.g. neural signals) have chaotic or fractal characteristics, which suggest that biological systems/networks could be analyzed effectively by applying nonlinear dynamics approaches involving chaos, fractal, and pattern formation etc. The concept of dynamical biomarkers was firstly introduced on a speech by A.L. Goldberger in 2006, which can be seen as an initiation of using nonlinear dynamical properties as biomarkers, although this concept has not yet been extended to the area of molecular networks.
Systems Biomarkers: Systems-level biomarkers, as an innovative concept, derive from the marriage of these two newly introduced concepts - network biomarkers and dynamical biomarkers. The goal of this specific Research Topic is to gather experts in both fields to shape a brand-new direction of identifying systems biomarkers for the next decade. This Research Topic welcomes original research findings, reviews, perspectives/opinions, as well as new insights and new approaches on this topic. The interesting area may include but are not limited to:
- Network-based genetic profile (e.g., gene expressions, gene mutations, or copy number variations) analysis/classification/prediction for complex diseases, such as various cancers, diabetes, or Alzheimer’s disease
- Identification of subnetwork markers, gene network signatures, pathway biomarkers, or pathway signatures for complex diseases
- Development of dynamical analysis approaches for complex molecular networks, such as protein-protein interaction networks, protein-compound interaction networks, gene regulatory networks, or metabolic networks
- Biomedical application of complex molecular network analysis based on machine learning, graph theory, Bayesian networks, Markov chain, random walk theory, flow simulation, or nonlinear dynamical modeling (e.g., ant colony optimization, multi-agent systems, and swarm intelligence)
- Dynamical modeling for neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis
- Dynamical modeling for neuron networks, neural systems, and neural development, et al.
- Chaotic dynamical analysis for neural signals, EEG, MEG signals, fMRI and PET images, et al.
- Fractal dynamical analysis of brain structures, neu
Research interest in identifying candidate genes/proteins as novel biomarkers for early detection, diagnosis, prognosis, or drug response continues to grow in the post-genome era. Identification of these genes and locus, while extremely valuable, is only the first step to the development of molecular profiling solutions for complex diseases, such as cancers, diabetes, and Alzheimer’s disease.
Network Biomarkers: From a view of network biology, the genes associated with a complex disease never function alone, but work together in a complex network. The concept of network biomarkers/signatures has already been proposed for candidate biomarker discovery by integrating disease susceptibility genes, gene expressions, and gene/protein networks together.
Complex Networks: Traditional network analyses often fail to find patterns in the ranked or clustered adjacency matrix of a complex network. Complex networks are often characterized by small-world and scale-free properties, which suggest that molecular entities may not have “absolute ranks” or “clear cluster boundary” among them. Could there be some emerging properties at systems-level for us to discover in complex molecular networks?
Dynamical Biomarkers: It has been found that both biological shapes (e.g. brain structures) and physiological signals (e.g. neural signals) have chaotic or fractal characteristics, which suggest that biological systems/networks could be analyzed effectively by applying nonlinear dynamics approaches involving chaos, fractal, and pattern formation etc. The concept of dynamical biomarkers was firstly introduced on a speech by A.L. Goldberger in 2006, which can be seen as an initiation of using nonlinear dynamical properties as biomarkers, although this concept has not yet been extended to the area of molecular networks.
Systems Biomarkers: Systems-level biomarkers, as an innovative concept, derive from the marriage of these two newly introduced concepts - network biomarkers and dynamical biomarkers. The goal of this specific Research Topic is to gather experts in both fields to shape a brand-new direction of identifying systems biomarkers for the next decade. This Research Topic welcomes original research findings, reviews, perspectives/opinions, as well as new insights and new approaches on this topic. The interesting area may include but are not limited to:
- Network-based genetic profile (e.g., gene expressions, gene mutations, or copy number variations) analysis/classification/prediction for complex diseases, such as various cancers, diabetes, or Alzheimer’s disease
- Identification of subnetwork markers, gene network signatures, pathway biomarkers, or pathway signatures for complex diseases
- Development of dynamical analysis approaches for complex molecular networks, such as protein-protein interaction networks, protein-compound interaction networks, gene regulatory networks, or metabolic networks
- Biomedical application of complex molecular network analysis based on machine learning, graph theory, Bayesian networks, Markov chain, random walk theory, flow simulation, or nonlinear dynamical modeling (e.g., ant colony optimization, multi-agent systems, and swarm intelligence)
- Dynamical modeling for neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis
- Dynamical modeling for neuron networks, neural systems, and neural development, et al.
- Chaotic dynamical analysis for neural signals, EEG, MEG signals, fMRI and PET images, et al.
- Fractal dynamical analysis of brain structures, neu
