Biomolecular Network Controllability With Drug Binding Information

Wu, Lin; Tang, Lingkai; Li, Min; Wu, Fang-Xiang

doi:10.1109/tnb.2017.2705106

Cited by 16 publications

(5 citation statements)

References 43 publications

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“…Drug targets, which are important biomarkers, can be interpreted as steering nodes in controlled biomolecular networks while drugs are viewed as the control signals. Wu et al [50] develop some seminal theories for the controllability of complex biomolecular networks and design several variants of the control centrality, which have been applied to identify drug targets from regulatory biological networks [51][52][53].…”

Section: Drug-related Biomarker Discoverymentioning

confidence: 99%

Network Learning for Biomarker Discovery

Ding¹,

Fu²,

Luo³

et al. 2023

IJNDI

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Survey/review study Network Learning for Biomarker Discovery Yulian Ding 1, Minghan Fu 1, Ping Luo 2, and Fang-Xiang Wu 1,3,4,* 1 Division of Biomedical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 2 Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada 3 Department of Computer Sciences, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 4 Department of Mechanical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada * Correspondence: faw341@mail.usask.ca Received: 14 October 2022 Accepted: 5 December 2022 Published: Abstract: Everything is connected and thus networks are instrumental in not only modeling complex systems with many components, but also accommodating knowledge about their components. Broadly speaking, network learning is an emerging area of machine learning to discover knowledge within networks. Although networks have permeated all subjects of sciences, in this study we mainly focus on network learning for biomarker discovery. We first overview methods for traditional network learning which learn knowledge from networks with centrality analysis. Then, we summarize the network deep learning, which are powerful machine learning models that integrate networks (graphs) with deep neural networks. Biomarkers can be placed in proper biological networks as vertices or edges and network learning applications for biomarker discovery are discussed. We finally point out some promising directions for future work about network learning.

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Section: Drug-related Biomarker Discoverymentioning

confidence: 99%

Network Learning for Biomarker Discovery

Ding¹,

Fu²,

Luo³

et al. 2023

IJNDI

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“…They are convinced that this will yield better results faster. In another paper Lin Wu et al [19] investigated the MSSs of biomolecular networks by considering the drug-protein binding information. They explain biomolecular network dynamics using linear timeinvariant dynamic model.…”

Section: Literature Surveymentioning

confidence: 99%

A Tool for Suggesting Ayurvedic Remedies from Curated and Classified Clinical Trial Reports

Raj¹,

Priya²

2018

IJET

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It requires great effort to search through huge number of published articles that provide information we need. Therefore it is necessary to find a solution that helps researchers in gaining accurate and deep understanding about diseases. Thus drug discovery and drug repurposing are gaining significance with the current onics tools. Traditional Medical practices like Ayurveda needs to be more visible to practitioners with evidence based approach. The clinical trials conducted have to be shared with the world for attaining the very philosophy of Ayurveda.. This paper presents a survey on various text mining technologies developed to classify theories and literature pertaining to the clinical observations of practitioners and suggests a possible solution to match a patient's symptoms.

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“…Recently, network controllability theory has made remarkable achievements in analyzing biological networks, such as Protein-Protein Interaction (PPI) network (20)(21)(22)(23)(24), brain network (25,26) and disease-related networks (27,28). Ryouji (20) applied network controllability theory on breast cancer gene expression networks, and designed a novel method to identify a set of critical control proteins that uniquely and structurally control the entire proteome.…”

Section: Introductionmentioning

confidence: 99%

Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN

Pan

Zhu

et al. 2021

Front. Oncol.

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BackgroundMYCN is an oncogenic transcription factor of the MYC family and plays an important role in the formation of tissues and organs during development before birth. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets.MethodsWe utilized network controllability theory, a recent developed powerful tool, to identify the potential drug target around MYCN based on Protein-Protein interaction network of MYCN. First, we constructed a Protein-Protein interaction network of MYCN based on public databases. Second, network control analysis was applied on network to identify driver genes and indispensable genes of the MYCN regulatory network. Finally, we developed a novel integrated approach to identify potential drug targets for regulating the function of the MYCN regulatory network.ResultsWe constructed an MYCN regulatory network that has 79 genes and 129 interactions. Based on network controllability theory, we analyzed driver genes which capable to fully control the network. We found 10 indispensable genes whose alternation will significantly change the regulatory pathways of the MYCN network. We evaluated the stability and correlation analysis of these genes and found EGFR may be the potential drug target which closely associated with MYCN.ConclusionTogether, our findings indicate that EGFR plays an important role in the regulatory network and pathways of MYCN and therefore may represent an attractive therapeutic target for cancer treatment.

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Biomolecular Network Controllability With Drug Binding Information

Cited by 16 publications

References 43 publications

Network Learning for Biomarker Discovery

Network Learning for Biomarker Discovery

A Tool for Suggesting Ayurvedic Remedies from Curated and Classified Clinical Trial Reports

Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN

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