An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction

Peng, Jiajie; Wang, Yuxian; Guan, Jiaojiao; Li, Jingyi; Han, Ruijiang; Hao, Jianye; Wei, Zhongyu; Shang, Xuequn

doi:10.1093/bib/bbaa430

Cited by 124 publications

(54 citation statements)

References 35 publications

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“…The generation and development of psychiatric disorders are influenced by genetic and environmental factors ( Sklar et al, 2011 ; Nagel et al, 2018 ; Ruderfer et al, 2018 ; Peng et al, 2020 ; Peng et al, 2021a ; Peng et al, 2021b ). For genetic factors, based on genome-wide association analysis (GWAS), Purcell et al implicate the major histocompatibility complex, constructed a polygenic risk score (PRS) of schizophrenia (SCZ) and verified that the PRS also predicted bipolar disorder (BD) ( Purcell et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

The Causal Effects of Insomnia on Bipolar Disorder, Depression, and Schizophrenia: A Two-Sample Mendelian Randomization Study

et al. 2021

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Psychiatric disorder, including bipolar disorder (BD), major depression (MDD), and schizophrenia (SCZ), affects millions of persons around the world. Understanding the disease causal mechanism underlying the three diseases and identifying the modifiable risk factors for them hold the key for the development of effective preventative and treatment strategies. We used a two-sample Mendelian randomization method to assess the causal effect of insomnia on the risk of BD, MDD, and SCZ in a European population. We collected one dataset of insomnia, three of BD, one of MDD, and three of SCZ and performed a meta-analysis for each trait, further verifying the analysis through extensive complementarity and sensitivity analysis. Among the three psychiatric disorders, we found that only insomnia is causally associated with MDD and that higher insomnia increases the risk of MDD. Specifically, the odds ratio of MDD increase of insomnia is estimated to be 1.408 [95% confidence interval (CI): 1.210–1.640, p = 1.03E-05] in the European population. The identified causal relationship between insomnia and MDD is robust with respect to the choice of statistical methods and is validated through extensive sensitivity analyses that guard against various model assumption violations. Our results provide new evidence to support the causal effect of insomnia on MDD and pave ways for reducing the psychiatric disorder burden.

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Section: Introductionmentioning

confidence: 99%

The Causal Effects of Insomnia on Bipolar Disorder, Depression, and Schizophrenia: A Two-Sample Mendelian Randomization Study

et al. 2021

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“…In recent years, predicting disease-related genes has drawn much attention in relative fields and many graph-based computational methods have performed proficiency in integrating large-scale omics data and disease phenotype ( Nguyen and Ho, 2012 ; Zemojtel et al, 2014 ; Kumar et al, 2018 ; Wang T. et al, 2020 ; Peng et al, 2021b ). It can be surmised that the prime cost of discovering effective drug targets will be decreased with the engagement of computational algorithms.…”

Section: Introductionmentioning

confidence: 99%

Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning

Liu

Hou

et al. 2021

Front. Genet.

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Cerebral ischemic stroke (IS) is a complex disease caused by multiple factors including vascular risk factors, genetic factors, and environment factors, which accentuates the difficulty in discovering corresponding disease-related genes. Identifying the genes associated with IS is critical for understanding the biological mechanism of IS, which would be significantly beneficial to the diagnosis and clinical treatment of cerebral IS. However, existing methods to predict IS-related genes are mainly based on the hypothesis of guilt-by-association (GBA). These methods cannot capture the global structure information of the whole protein–protein interaction (PPI) network. Inspired by the success of network representation learning (NRL) in the field of network analysis, we apply NRL to the discovery of disease-related genes and launch the framework to identify the disease-related genes of cerebral IS. The utilized framework contains three main parts: capturing the topological information of the PPI network with NRL, denoising the gene feature with the participation of a stacked autoencoder (SAE), and optimizing a support vector machine (SVM) classifier to identify IS-related genes. Superior to the existing methods on IS-related gene prediction, our framework presents more accurate results. The case study also shows that the proposed method can identify IS-related genes.

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“…There are matrix factorization-based graph embedding methods [such as IMC ( Natarajan and Dhillon, 2014 ) and PCFM ( Zeng et al, 2017 )], and also methods based on skip-gram based neuron networks [such as LINE ( Tang et al, 2015 ), DeepWalk ( Perozzi et al, 2014 ), and Node2Vec ( Grover and Leskovec, 2016 )], and graph neuron networks [such as graph convolutional network ( Wu et al, 2020 )]. These techniques have been widely used in bioinformatics applications such as the discovery of antibiotics ( Stokes et al, 2020 ), disease genes ( Peng et al, 2021b ), disease modules ( Wang et al, 2020 ), drug targets ( Peng et al, 2021c ), drug side-effects ( Han et al, 2021 ), RNA-targets ( Peng et al, 2019b ), molecular network edges ( Perozzi et al, 2014 ; Ribeiro et al, 2017 ; Peng et al, 2021d ), etc. However, there has been a lack of research on discovering genes associated with diabetes mellitus using cutting-edge graph-embedding techniques.…”

Section: Introductionmentioning

confidence: 99%

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

Lin

Ruan

et al. 2021

Front. Genet.

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Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.

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An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction

Cited by 124 publications

References 35 publications

The Causal Effects of Insomnia on Bipolar Disorder, Depression, and Schizophrenia: A Two-Sample Mendelian Randomization Study

The Causal Effects of Insomnia on Bipolar Disorder, Depression, and Schizophrenia: A Two-Sample Mendelian Randomization Study

Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

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