Dataset Information

Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

ABSTRACT:

Background

A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner.

Results

We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant network motifs, further validated by expert knowledge and functional enrichment analyses.

Conclusions

We have developed a novel network-based computational approach to investigate the heterogeneous drug-gene-disease network extracted from Semantic MEDLINE. We demonstrate the power of this approach by prioritizing candidate disease genes, inferring potential disease relationships, and proposing novel drug targets, within the context of the entire knowledge. The results indicate that such approach will facilitate the formulization of novel research hypotheses, which is critical for translational medicine research and personalized medicine.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC4137727 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

Zhang Yuji Y Tao Cui C Jiang Guoqian G Nair Asha A AA Su Jian J Chute Christopher G CG Liu Hongfang H

Journal of biomedical semantics 20140806

<h4>Background</h4>A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and ...[more]

PMID: 25170419

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Project description:BackgroundDiverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network.ResultsWe developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease.ConclusionWe propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC).

Project description:ImportanceClearer delineation of the phenotypic heterogeneity within behavioral variant frontotemporal dementia (bvFTD) will help uncover underlying biological mechanisms and improve clinicians' ability to predict disease course and to design targeted management strategies.ObjectiveTo identify subtypes of bvFTD syndrome based on distinctive patterns of atrophy defined by selective vulnerability of specific functional networks targeted in bvFTD using statistical classification approaches.Design, setting and participantsIn this retrospective observational study, 90 patients meeting the Frontotemporal Dementia Consortium consensus criteria for bvFTD underwent evaluation at the Memory and Aging Center of the Department of Neurology at University of California, San Francisco. Patients underwent a multidisciplinary clinical evaluation, including clinical demographics, genetic testing, symptom evaluation, neurologic examination, neuropsychological bedside testing, and socioemotional assessments. All patients underwent structural magnetic resonance imaging at their earliest evaluation at the memory clinic. From each patient's structural imaging scans, the mean volumes of 18 regions of interest (ROI) constituting the functional networks specifically vulnerable in bvFTD, including the salience network (SN), with key nodes in the frontoinsula and pregenual anterior cingulate, and the semantic appraisal network (SAN), anchored in the anterior temporal lobe and subgenual cingulate, were estimated. Principal component and cluster analyses of ROI volumes were used to identify patient clusters with anatomically distinct atrophy patterns. Data were collected from from June 19, 2002, to January 13, 2015.Main outcomes and measuresEvaluation of brain morphology and other clinical features, including presenting symptoms, neurologic examination signs, neuropsychological performance, rate of dementia progression, and socioemotional function, in each patient cluster.ResultsNinety patients (54 men [60%]; 36 women [40%]; mean [SD] age at evaluation, 55.1 [9.7] years) were included in the analysis. Four subgroups of patients with bvFTD with distinct anatomic patterns of network degeneration were identified, including 2 salience network-predominant subgroups (frontal/temporal [SN-FT] and frontal [SN-F]), a semantic appraisal network-predominant group (SAN), and a subcortical-predominant group. Subgroups demonstrated distinct patterns of cognitive, socioemotional, and motor symptoms, as well as genetic compositions and estimated rates of disease progression.Conclusions and relevanceDivergent patterns of vulnerability in specific functional network components make an important contribution to the clinical heterogeneity of bvFTD. The data-driven anatomic classification identifies biologically meaningful anatomic phenotypes and provides a replicable approach to disambiguate the bvFTD syndrome.

Dataset Information

Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

Background

Results

Conclusions

Publications

Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

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