Network-based Phenome-Genome Association Prediction by Bi-Random Walk

Xie, Mao Qiang; Xu, Ying; Zhang, Yao Gong; Hwang, Tae Hyun; Kuang, Rui

doi:10.1371/journal.pone.0125138

Cited by 43 publications

(38 citation statements)

References 43 publications

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“…Higher values of semantic similarity indicate greater specificity in the common pathophenotypic space between a pair of genes. It is known that ontology-based phenotypic similarity methods can also contribute to improve disease-causing gene networks based on phenotypic information built with text-mining analysis [42] or random-walk trajectories between genes considering the ontology as a simple graph [43].…”

Section: Resultsmentioning

confidence: 99%

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

et al. 2013

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The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, “the human diseases networks” (HDN) and “the orphan disease networks” (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the “Human Phenotype Ontology”. The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.

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Section: Resultsmentioning

confidence: 99%

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

et al. 2013

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“…In this section, we apply the proposed NoN model to the candidate gene prioritization problem, which has recently attracted in- [29], alignment based methods [30], random walk based methods [31,14,28], and maximum flow based methods [3].…”

Section: Protein Interaction Nonmentioning

confidence: 99%

“…The resulting disease-gene associations involve 147 associations between 106 diseases and 102 genes. These genes are We select four state-of-the-art methods for comparison, including RWRH [14], BIRW [31], PRINCE [28] and Katz [20]. We use the standard leave-one-out cross validation to compare the prioritization accuracy of the selected methods.…”

Section: Protein Interaction Nonmentioning

confidence: 99%

Inside the atoms

Tong

Wang

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Networks are prevalent and have posed many fascinating research questions. How can we spot similar users, e.g., virtual identical twins, in Cleveland for a New Yorker? Given a query disease, how can we prioritize its candidate genes by incorporating the tissuespecific protein interaction networks of those similar diseases? In most, if not all, of the existing network ranking methods, the nodes are the ranking objects with the finest granularity.In this paper, we propose a new network data model, a Network of Networks (NoN), where each node of the main network itself can be further represented as another (domain-specific) network. This new data model enables to compare the nodes in a broader context and rank them at a finer granularity. Moreover, such an NoN model enables much more efficient search when the ranking targets reside in a certain domain-specific network. We formulate ranking on NoN as a regularized optimization problem; propose efficient algorithms and provide theoretical analysis, such as optimality, convergence, complexity and equivalence. Extensive experimental evaluations demonstrate the effectiveness and the efficiency of our methods.

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“…However, the reverse is also true. Using a bi-random walk (BiRW) algorithm, phenotype-gene association network patterns in circular bigraph format have been utilized to yield the associations between disease phenotypes and genes (Xie, Hwang and Kuang, 2012). This method identifies and quantifies an enrichment of “behavior”, “synaptic transmission”, and “transmission of nerve impulse” by the causative genes of psychiatric diseases.…”

Section: Bridging Across Systems From Molecule To Phenotypementioning

confidence: 99%

Systems biology of complex symptom profiles: Capturing interactivity across behavior, brain and immune regulation

Broderick

Craddock

2013

Brain, Behavior, and Immunity

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As our thinking about the basic principles of biology and medicine continue to evolve, the importance of context and regulatory interaction is becoming increasingly obvious. Biochemical and physiological components do not exist in isolation but instead are part of a tightly integrated network of interacting elements that ensure robustness and support the emergence of complex behavior. This integration permeates all levels of biology from gene regulation, to immune cell signaling, to coordinated patterns of neuronal activity and the resulting psychosocial interaction. Systems biology is an emerging branch of science that sits as a translational catalyst at the interface of the life and computational sciences. While there is no universally accepted definition of systems biology, we attempt to provide an overview of some the basic unifying concepts and current efforts in the field as they apply to illnesses where brain and subsequent behavior are a chief component, for example autism, schizophrenia, depression, and others. Methods in this field currently constitute a broad mosaic that stretches across multiple scales of biology and physiological compartments. While this work by no means constitutes an exhaustive list of all these methods, this work highlights the principal sub-disciplines presently driving the field as well as future directions of progress.

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Network-based Phenome-Genome Association Prediction by Bi-Random Walk

Cited by 43 publications

References 43 publications

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

Inside the atoms

Systems biology of complex symptom profiles: Capturing interactivity across behavior, brain and immune regulation

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