Identification of novel immune‐relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis

Han, Zhijie; Xue, Weiwei; Lin, Tao; Zhu, Feng

doi:10.1111/cns.13051

Cited by 30 publications

(15 citation statements)

References 103 publications

(103 reference statements)

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“…Eighteen potential drug target genes were identified, and thirteen of them had been reported to be closely associated with AD. Enrichment analyses of these identified drug target genes, based on nine pathway-related databases, revealed that four of those identified drug target genes are involved in the classical complement pathway and the process of presenting antigens [ 43 ].…”

Section: C Onversion To Admentioning

confidence: 99%

Synaptic Pruning in Alzheimer’s Disease: Role of the Complement System

Brucato¹,

Benjamin²

2020

GJMR

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Section: C Onversion To Admentioning

confidence: 99%

Synaptic Pruning in Alzheimer’s Disease: Role of the Complement System

Brucato¹,

Benjamin²

2020

GJMR

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“…As reported, the human PPI network properties and biological system profile were key factors determining efficacy-safety balance (Zheng et al, 2006 ; Ragusa et al, 2010 ; Guo et al, 2018 ). Network properties were inherent feature of a target in the human PPI network, while biological system profile could reflect both the on-target and off-target pharmacology (Bender et al, 2007 ; Han et al, 2018 ; Zhu et al, 2018 ). Herein, 32 features of human PPI network together with 4 biological system properties were therefore adopted and calculated for further analyses.…”

Section: Resultsmentioning

confidence: 99%

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

Yin

Tang

et al. 2018

Front. Pharmacol.

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One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

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“…Han et al [9], harnessed the power of ontology inferencing combined with network analysis to identify new immunerelevant drug-target genes for Alzheimer's Disease (AD). This was working on the theory that due to the strong relation of AD to the immune system, there were potentially undiscovered immune-relevant AD drug-target genes that could help combat the disease.…”

Section: Semantic Applications In Drug Discoverymentioning

confidence: 99%

“…There has been a wave of computation-based innovation around creating tools to aid drug discovery [8,9,10] and many of these tools utilize semantic web technologies. This is both due to data management capabilities of the semantic tools coupled with the researcher's recognition that purely integrating sources together is not enough.…”

Section: Introductionmentioning

confidence: 99%

A new wave of innovation in Semantic web tools for drug discovery

Kanza

Frey

2019

Expert Opinion on Drug Discovery

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Introduction: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to dealing with the high levels of data for drug discovery. These technologies enable us to represent the data in a formal, structured, interoperable and comparable way, and to tease out undiscovered links between drug data (be it identifying new drug-targets or relevant compounds, or links between specific drugs and diseases). Areas covered: This review focuses on explaining how semantic web technologies are being used to aid advances in drug discovery. The main types of semantic web technologies are explained, outlining how they work and how they can be used in the drug discovery process, with a consideration of how the use of these technologies has progressed from their initial usage. Expert opinion: The increased availability of shared semantic resources (tools, data and importantly the communities) have enabled the application of semantic web technologies to facilitate semantic (context dependent) search across multiple data sources, which can be used by machine learning to produce better predictions by exploiting the semantic links in knowledge graphs and linked datasets.

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Identification of novel immune‐relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis

Cited by 30 publications

References 103 publications

Synaptic Pruning in Alzheimer’s Disease: Role of the Complement System

Synaptic Pruning in Alzheimer’s Disease: Role of the Complement System

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

A new wave of innovation in Semantic web tools for drug discovery

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