Identifying critical transitions of complex diseases based on a single sample

Li, Rui; Yu, Xiangtian; Liu, Xiaoping; Xu, Dong; Aihara, Kazuyuki; Chen, Luonan

doi:10.1093/bioinformatics/btu084

Cited by 85 publications

(73 citation statements)

References 29 publications

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“…Finally, the association between edge signatures and NSCLC metastasis was analyzed, and it demonstrates that Bedge^biomarkers may have significant contributions to precise medicine and personalized medicine. To further improve the accuracy of the CDI-based method, deriving appropriate subtype criteria and integrating multiple omics data for network biomarkers and further dynamical network biomarkers [66][67][68][69] is one of the future topics.…”

Section: Discussionmentioning

confidence: 99%

Detecting disease genes of non-small lung cancer based on consistently differential interactions

Shi

Zeng

et al. 2015

Cancer Metastasis Rev

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Systematic identification of causal disease genes can shed light on the mechanisms underlying complex diseases and provide crucial information to develop efficient biomarkers or design suitable therapies. The present paper describes a novel approach to detect potential disease genes for lung cancer, based on consistently differential interaction (CDI) scheme from heterogeneous disease datasets. In particular, reliable disordered regulations in disease states were discovered by identifying the CDIs, from which the disease genes were further detected based on their topological structures on the network. As an application of the CDI-based method, the RNA-seq data of two subtypes of non-small lung cancer were used to identify CDIs from normal to cancer onset. The results of analysis well agree with the prior knowledge as well as the experiments, thereby implying the predictive power of the CDI-based method. The comparison with other approaches also indicated the superiority of the CDI-based method in terms of accuracy and effectiveness on detecting disease-specific genes for lung cancer and metastasis. In contrast to conventional molecular biomarkers, the identified CDIs as novel network biomarkers or edge biomarkers can be applied to predict patient survival for both subtypes of lung cancers, and the interactions among CDIs can be further used as new edgetic targets for network drug design. In addition, a potential molecular mechanism was developed to explain the key roles of the identified CDIs in lung cancer and metastasis from a network perspective.

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Section: Discussionmentioning

confidence: 99%

Detecting disease genes of non-small lung cancer based on consistently differential interactions

Shi

Zeng

et al. 2015

Cancer Metastasis Rev

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“…To address this problem, several studies have investigated the edge biomarkers of single samples. Mathematical and computational approaches for phenotype prediction are broadly classifiable into multi-sample-based approaches (if multiple samples are available for use in the prediction step) [50] and single-sample-based approaches (if a single sample is available) [10]. Note that a group of nodes constitutes a node set, but a group of edges is a network.…”

Section: Edge Biomarkers For Classification and Prediction With Netwomentioning

confidence: 99%

“…A single-sample-based approach for Class II dynamical network and edge biomarkers is the DNB-S scoring method of Liu et al [10]. This method was developed to identify the pre-disease states of single samples (given a group of normal control samples) by exploring the distributions of differential expressions of the pre-detected DNB and non-DNB molecules [10].…”

Section: Dynamical Network Biomarkers and Dynamical Edge Biomarkersmentioning

confidence: 99%

“…This method was developed to identify the pre-disease states of single samples (given a group of normal control samples) by exploring the distributions of differential expressions of the pre-detected DNB and non-DNB molecules [10]. Intuitively, the expressions of DNB molecules should display a double-peak (bimodal) distribution in the pre-disease state but a single-peak distribution in the normal state.…”

Section: Dynamical Network Biomarkers and Dynamical Edge Biomarkersmentioning

confidence: 99%

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Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

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“…In our previous work (Yu et al, 2011), we used the Kullback-Leibler divergence (also called relative entropy) to obtain a symmetrised measure and successfully studied the phylogeny of various DNA sequences based on their probability distributions. The Kullback-Leibler divergence, which is a powerful tool to measure the difference between two probability distributions, has been widely used in many fields, such as bioinformatics (Kaitchenko, 2004; Liu et al, 2014; McClendon et al, 2012; Hoang et al, 2015), genomics (Huang et al, 2014; Lin, 2015; Siegel et al, 2015), and machine learning (Lee and Lee, 2006; Rubinstein and Kroese, 2013). By means of this measure, we performed cluster analysis for those subjects including MDD cases and controls.…”

Section: Introductionmentioning

confidence: 99%

Whole-genome single nucleotide variant distribution on genomic regions and its relationship to major depression

Baune

Licinio

et al. 2017

Psychiatry Research

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Recent advances in DNA technologies have provided unprecedented opportunities for biological and medical research. In contrast to current popular genotyping platforms which identify specific variations, whole-genome sequencing (WGS) allows for the detection of all private mutations within an individual. Major depressive disorder (MDD) is a chronic condition with enormous medical, social and economic impacts. Genetic analysis, by identifying risk variants and thereby increasing our understanding of how MDD arises, could lead to improved prevention and the development of new and more effective treatments. Here we investigated the distributions of whole-genome single nucleotide variants (SNVs) on 12 different genomic regions for 25 human subjects using the symmetrised Kullback-Leibler divergence to measure the similarity between their SNV distributions. We performed cluster analysis for MDD patients and ethnically matched healthy controls. The results showed that Mexican-American controls grouped closer; in contrast depressed Mexican-American participants grouped away from their ethnically matched controls. This implies that whole-genome SNV distribution on the genomic regions may be related to major depression.

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Identifying critical transitions of complex diseases based on a single sample

Cited by 85 publications

References 29 publications

Detecting disease genes of non-small lung cancer based on consistently differential interactions

Detecting disease genes of non-small lung cancer based on consistently differential interactions

Edge biomarkers for classification and prediction of phenotypes

Whole-genome single nucleotide variant distribution on genomic regions and its relationship to major depression

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