scholarly journals A network-based method for predicting disease-associated enhancers

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260432
Author(s):  
Duc-Hau Le
Keyword(s):  
Random Walk ◽  
Heterogeneous Network ◽  
Target Genes ◽  
Interaction Network ◽  
Potential Method ◽  
Diffusion Method ◽  
Random Walk With Restart ◽  
Functional Interaction ◽  
Functional Interaction Network ◽  
Network Diffusion

Background Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. Results In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

scholarly journals ASpediaFI: Functional interaction analysis of alternative splicing events

2020 ◽  
Author(s):  
Doyeong Yu ◽  
Kyubin Lee ◽  
Daejin Hyung ◽  
Soo Young Cho ◽  
Charny Park
Keyword(s):  
Random Walk ◽  
Alternative Splicing ◽  
Biological Process ◽  
Interaction Analysis ◽  
Interaction Network ◽  
Gene Interactions ◽  
Random Walk With Restart ◽  
Functional Interaction ◽  
Global Regulation ◽  
Simulation Data

ABSTRACTAlternative splicing (AS) regulates biological process governing phenotype or disease. However, it is challenging to systemically analyze global regulation of AS events, their gene interactions, and functions. Here, we introduce a novel application, ASpediaFI for identifying AS events and co-regulated gene interactions implicated in pathways. Our method establishes an interaction network including AS events, performs random walk with restart, and finally identifies a functional subnetwork containing the AS event. We validated the capability of ASpediaFI to interpret biological relevance based on three case studies. Using simulation data, we achieved higher accuracy than with other methods and detected pathway-associated AS events.

scholarly journals Visualization of drug target interactions in the contexts of pathways and networks with ReactomeFIViz

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 908 ◽  
Author(s):  
Aurora S. Blucher ◽  
Shannon K. McWeeney ◽  
Lincoln Stein ◽  
Guanming Wu
Keyword(s):  
Targeted Therapies ◽  
Biological Networks ◽  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Patient Specific ◽  
Functional Interaction ◽  
Functional Interaction Network ◽  
A Genome

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

scholarly journals A scalable random walk with restart on heterogeneous networks with Apache Spark for ranking disease-causing genes using type-2 fuzzy data fusion

2019 ◽  
Author(s):  
Mehdi Joodaki ◽  
Nasser Ghadiri ◽  
Zeinab Maleki ◽  
Maryam Lotfi Shahreza
Keyword(s):  
Random Walk ◽  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Gene Network ◽  
Parallel Execution ◽  
Apache Spark ◽  
Random Walk With Restart ◽  
Integrated Network ◽  
New Genes ◽  
Gene Similarity

AbstractPrediction and discovery of disease-causing genes are among the main missions of biology and medicine. In recent years, researchers have developed several methods based on gene/protein networks for the detection of causative genes. However, because of the presence of false positives in these networks, the results of these methods often lack accuracy and reliability. This problem can be solved by using multiple genomic sources to reduce noise in data. However, network integration can also affect the quality of the integrated network. In this paper, we present a method named RWRHN (random walk with restart on a heterogeneous network) with fuzzy fusion or RWRHN-FF. In this method, first, four gene-gene similarity networks are constructed based on different genomic sources and then integrated using the type-II fuzzy voter scheme. The resulting gene-gene network is then linked to a disease-disease similarity network, which itself is constructed by the integration of four sources, through a two-part disease-gene network. The product of this process is a reliable heterogeneous network, which is analyzed by the RWRHN algorithm. The results of the analysis with the leave-one-out cross-validation method show that RWRHN-FF outperforms both RWRHN and RWRH. The proposed method is used to predict new genes for prostate, breast, gastric and colon cancers. To reduce the algorithm run time, Apache Spark is used as a platform for parallel execution of the RWRHN algorithm on heterogeneous networks. In the test conducted on heterogeneous networks of different sizes, this solution results in faster convergence than other non-distributed modes of implementations.

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