Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION

Sumathipala, Marissa; Maiorino, Enrico; Weiss, Scott T.; Sharma, Amitabh

doi:10.3389/fphys.2019.00888

Cited by 26 publications

(15 citation statements)

References 93 publications

(110 reference statements)

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“…Network diffusion (or network propagation) is a methodology able to identify those genes which are proximal to a starting list of seed genes by using network topology (and optionally other features). In network medicine it can be used to identify genes and genetic modules that underlie human diseases ( Mosca et al, 2014 ; Cowen et al, 2017 ; Sumathipala et al, 2019 ) or to identify causal paths linking mutations to expression regulators, or to discover significantly mutated subnetworks in cancer ( Vandin et al, 2011 ; Paull et al, 2013 ). The methodology exploits the concept of heat diffusion, i.e., how the heat distribution spreads over time in a medium, here consisting of the PPI network, as it flows from nodes where it is higher toward nodes where it is lower according to the diffusion coefficient and their mutual connections.…”

Section: Methodsmentioning

confidence: 99%

Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19

Stolfi

Manni

Soligo

et al. 2020

Front. Cell Dev. Biol.

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The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle.

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Section: Methodsmentioning

confidence: 99%

Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19

Stolfi

Manni

Soligo

et al. 2020

Front. Cell Dev. Biol.

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“…Gene-disease networks, drug-target networks, or pathway-gene networks have previously mostly been constructed and analyzed in unweighted form Goh et al ( 2007 ), He et al ( 2014 ), and Halu et al ( 2019 ). However, they can also be estimated in weighted form by including, for example, information on predictions or associations in the edge weights (Sumathipala et al, 2019 ). While regulatory networks and eQTL networks are sometimes unweighted, they are more often based on likelihoods or associations.…”

Section: Community Detection Strategiesmentioning

confidence: 99%

“…However, many types of biological networks are naturally bipartite, meaning that there are two disjoint types of nodes, and interactions can only form between the different node types. Examples of genome-wide bipartite networks are gene regulatory networks (Emmert-Streib et al, 2014 )—which include transcriptional, post-transcriptional, and post-translational regulatory networks (Koch, 2016 ; Statello et al, 2020 ; Guo and Amir, 2021 )—eQTL networks, networks comprising gene-pathway associations (He et al, 2014 ), networks representing gene-disease (Goh et al, 2007 ; Halu et al, 2019 ) or non-coding RNA (ncRNA)-disease associations (Sumathipala et al, 2019 ), or drug-target interaction networks (Yildirim et al, 2007 ) (see Pavlopoulos et al, 2018 for an extensive overview of different types of bipartite biological networks).…”

Section: Introductionmentioning

confidence: 99%

Community Detection in Large-Scale Bipartite Biological Networks

Calderer

Kuijjer

2021

Front. Genet.

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Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

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“…Li et al developed a local random walk based LDA prediction model (LRWHLDA) [21]. Sumathipala et al developed a network diffusion based LDA prediction model by integrating the proteindisease, protein-lncRNA and protein-protein associations [22]. Zhang et al developed a DeepWalk based LDA prediction model by integrating the miRNA-disease, lncRNAdisease, and miRNA-lncRNA associations [23].…”

Section: Introductionmentioning

confidence: 99%

A random forest based computational model for predicting novel lncRNA-disease associations

Yao

Zhan

et al. 2020

BMC Bioinformatics

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Background: Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results: To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions: Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

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Network Diffusion Approach to Predict LncRNA Disease Associations Using Multi-Type Biological Networks: LION

Cited by 26 publications

References 93 publications

Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19

Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19

Community Detection in Large-Scale Bipartite Biological Networks

A random forest based computational model for predicting novel lncRNA-disease associations

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