scholarly journals CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Wang ◽  
Xi Yang ◽  
Chengkun Wu ◽  
Canqun Yang
Keyword(s):  
Biological Network ◽  
Gene Interaction ◽  
Gene Interactions ◽  
Target Node ◽  
Convolutional Network ◽  
Initial Node ◽  
Interactive Relation ◽  
Drug Leads ◽  
Node Embeddings ◽  
Novel Therapeutic

Abstract Background Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions. Results We present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types. Conclusions We study three model implementations CGINet-1/2/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.

Genetic, morphometric, and molecular analyses of interspecies differences in head shape and hybrid developmental defects in the wasp genus Nasonia

2019 ◽  
Author(s):  
Lorna B Cohen ◽  
Rachel Edwards ◽  
Dyese Moody ◽  
Deanna Arsala ◽  
Jack H Werren ◽  
...  
Keyword(s):  
Parasitoid Wasp ◽  
Gene Interaction ◽  
Distinct Species ◽  
Diploid Male ◽  
Gene Interactions ◽  
Morphological Development ◽  
Head Shape ◽  
Developmental Defects ◽  
Primary Driver ◽  
Species Specific

AbstractMales in the parasitoid wasp genus Nasonia (N. vitripennis, N. giraulti, N. longicornis) have distinct, species specific, head shapes. Fertile hybrids among the species are readily produced in the lab allowing genetic analysis of the evolved differences. In addition, the obligate haploidy of males makes these wasps a uniquely powerful model for analyzing the role of complex gene interactions in development and evolution. Previous analyses have shown that complex gene interactions underpin different aspects of the shape differences, and developmental incompatibilities that are specific to the head in F2 haploid hybrid males are also governed by networks of gene interaction. Here we use the genetic tools available in Nasonia to extend our understanding of the gene interactions that affect development and morphogenesis in male heads. Using artificial diploid male hybrids, we show that alleles affecting head shape are codominant, leading to uniform, averaged hybrid F1 diploid male heads, while the alleles mediating developmental defects are recessive, and are not visible in the diploid hybrids. We also determine that divergence in time, rather than in morphological disparity is the primary driver of hybrid developmental defects. In addition, we show that doublesex is necessary for the male head shape differences, but is not the only important factor. Finally we demonstrate that we can dissect complex interspecies gene interaction networks using introgression in this system. These advances represent significant progress in the complex web of gene interactions that govern morphological development, and chart the connections between genomic and phenotypic variation.

scholarly journals Biological network analysis with deep learning

2020 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O’Bray ◽  
Karsten Borgwardt
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Biological Networks ◽  
Protein Function ◽  
Regulatory Networks ◽  
Biological Network ◽  
Gene Interaction ◽  
Disease Diagnosis ◽  
Interaction Prediction ◽  
Graph Neural Networks

Abstract Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

scholarly journals An Exploration of Gene-Gene Interactions and Their Effects on Hypertension

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Meng ◽  
Susan Groth ◽  
Jill R. Quinn ◽  
John Bisognano ◽  
Tong Tong Wu
Keyword(s):  
Interaction Analysis ◽  
Association Studies ◽  
Independent Set ◽  
Gene Interaction ◽  
Single Locus ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Original Cohort ◽  
Genome Wide ◽  
Heart Study

Hypertension tends to perpetuate in families and the heritability of hypertension is estimated to be around 20–60%. So far, the main proportion of this heritability has not been found by single-locus genome-wide association studies. Therefore, the current study explored gene-gene interactions that have the potential to partially fill in the missing heritability. A two-stage discovery-confirmatory analysis was carried out in the Framingham Heart Study cohorts. The first stage was an exhaustive pairwise search performed in 2320 early-onset hypertensive cases with matched normotensive controls from the offspring cohort. Then, identified gene-gene interactions were assessed in an independent set of 694 subjects from the original cohort. Four unique gene-gene interactions were found to be related to hypertension. Three detected genes were recognized by previous studies, and the other 5 loci/genes (MAN1A1, LMO3, NPAP1/SNRPN, DNAL4, and RNA5SP455/KRT8P5) were novel findings, which had no strong main effect on hypertension and could not be easily identified by single-locus genome-wide studies. Also, by including the identified gene-gene interactions, more variance was explained in hypertension. Overall, our study provides evidence that the genome-wide gene-gene interaction analysis has the possibility to identify new susceptibility genes, which can provide more insights into the genetic background of blood pressure regulation.

scholarly journals USE OF MOLECULAR GENETIC METHODS IN THE STUDY OF HEREDITARY PREDISPOSITION TO ATOPIC DISEASES INCHILDREN

2011 ◽  
Vol 8 (3) ◽  
pp. 48-55
Author(s):  
E S Tyumentseva ◽  
N V Petrova ◽  
I I Balabolkin ◽  
V G Pinelis ◽  
E S Tumentseva ◽  
...  
Keyword(s):  
Reduction Method ◽  
Molecular Genetic ◽  
Gene Interaction ◽  
Atopic Disease ◽  
Gene Interactions ◽  
Atopic Diseases ◽  
European Part Of Russia ◽  
Polymorphic Variants ◽  
Significant Model ◽  
Dimentionality Reduction

Background. Study of the аssociations of susceptibility genes to the development of atopic diseases in children. Materials and methods. All 325 examined children reside on the territory of the European part of Russia who by according to surveys, Russian by nationality. Analysis of polymorphism in genes of receptors ADRB2, GRL, ALOX5, genes of biotransformation - CYP1A1, CYP2C9, CYP2C19, GSTT1, GSTM1, NAT2 as well as the variants of the genes MTHFR and TNFA was performed in patients suffering from atopic disease and in healthy individuals. Using Multifactor Dimentionality Reduction method (MDR) it was defined the most significant model of genegene interaction for the development of atopic disease Results. Association of the development of atopic diseases with polymorphic variants of the genes: ALOX5 (VNTR) GRL (1220A > G) TNFA (-308G > A) CYP1A1 (6235T > C) and GSTM1 was identified in surveyed children. The highrisk alleles and genotypes of developing atopic diseases in pediatric patients were determined. Using Multifactor Dimentionality Reduction method (MDR) it was defined the most significant model of gene-gene interaction for the development of atopic disease, including ADRB2 (79 C >G), (46A > G), CYP2C19 (G681A) was defined. Conclusion. There were identified polymorphic variants of genes and important gene-gene interactions associated with development of atopic diseases in children.

scholarly journals Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

2021 ◽  
Vol 9 ◽  
Author(s):  
Yingjie Guo ◽  
Chenxi Wu ◽  
Zhian Yuan ◽  
Yansu Wang ◽  
Zhen Liang ◽  
...  
Keyword(s):  
Association Studies ◽  
Real Data ◽  
Gene Interaction ◽  
Genome Wide Association ◽  
Superior Performance ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
The Difference

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

scholarly journals DGIdb 3.0: a redesign and expansion of the drug-gene interaction database

2017 ◽  
Author(s):  
Kelsy C. Cotto ◽  
Alex H. Wagner ◽  
Yang-Yang Feng ◽  
Susanna Kiwala ◽  
Adam C. Coffman ◽  
...  
Keyword(s):  
User Interface ◽  
Web Application ◽  
Response Times ◽  
Gene Interaction ◽  
Application Programming Interface ◽  
Gene Interactions ◽  
Application Framework ◽  
Interaction Database ◽  
Search Filters ◽  
Ease Of Access

ABSTRACTThe Drug-Gene Interaction Database (DGIdb, www.dgidb.org) consolidates, organizes, and presents drug-gene interactions and gene druggability information from papers, databases, and web resources. DGIdb normalizes content from more than thirty disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API), and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included twenty-eight sources were updated. Six new resources were added, bringing the total number of sources to thirty-three. These updates and additions of sources have cumulatively resulted in 56,309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and antineoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times, and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes, and drug-gene interactions, including listings of PubMed IDs (PMIDs), interaction type, and other interaction metadata.

Determining dependency and redundancy for identifying gene–gene interaction associated with complex disease

2020 ◽  
Vol 18 (05) ◽  
pp. 2050035
Author(s):  
Xiangdong Zhou ◽  
Keith C. C. Chan ◽  
Zhihua Huang ◽  
Jingbin Wang
Keyword(s):  
Genetic Variants ◽  
Conditional Independence ◽  
Complex Disease ◽  
Disease Status ◽  
Gene Interaction ◽  
Interesting Property ◽  
Gene Interactions ◽  
Definition Of ◽  
The Relationship ◽  
Novel Algorithm

As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene–gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to [Formula: see text] genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene–gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene–gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene–gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.

scholarly journals miRactDB characterizes miRNA–gene relation switch between normal and cancer tissues across pan-cancer

2020 ◽  
Author(s):  
Hua Tan ◽  
Pora Kim ◽  
Peiqing Sun ◽  
Xiaobo Zhou
Keyword(s):  
Mirna Gene ◽  
Gene Interaction ◽  
Histological Change ◽  
Gene Interactions ◽  
Promoter Regions ◽  
Critical Set ◽  
Normal Tissues ◽  
Gene Coding ◽  
Cancer Tissues ◽  
Pan Cancer

Abstract It has been increasingly accepted that microRNA (miRNA) can both activate and suppress gene expression, directly or indirectly, under particular circumstances. Yet, a systematic study on the switch in their interaction pattern between activation and suppression and between normal and cancer conditions based on multi-omics evidences is not available. We built miRactDB, a database for miRNA–gene interaction, at https://ccsm.uth.edu/miRactDB, to provide a versatile resource and platform for annotation and interpretation of miRNA–gene relations. We conducted a comprehensive investigation on miRNA–gene interactions and their biological implications across tissue types in both tumour and normal conditions, based on TCGA, CCLE and GTEx databases. We particularly explored the genetic and epigenetic mechanisms potentially contributing to the positive correlation, including identification of miRNA binding sites in the gene coding sequence (CDS) and promoter regions of partner genes. Integrative analysis based on this resource revealed that top-ranked genes derived from TCGA tumour and adjacent normal samples share an overwhelming part of biological processes, which are quite different than those from CCLE and GTEx. The most active miRNAs predicted to target CDS and promoter regions are largely overlapped. These findings corroborate that adjacent normal tissues might have undergone significant molecular transformations towards oncogenesis before phenotypic and histological change; and there probably exists a small yet critical set of miRNAs that profoundly influence various cancer hallmark processes. miRactDB provides a unique resource for the cancer and genomics communities to screen, prioritize and rationalize their candidates of miRNA–gene interactions, in both normal and cancer scenarios.

scholarly journals Temporal network embedding using graph attention network

2021 ◽  
Author(s):  
Anuraj Mohan ◽  
K V Pramod
Keyword(s):  
Information Matrix ◽  
Temporal Correlation ◽  
Temporal Network ◽  
Convolutional Network ◽  
Attention Network ◽  
Time Varying Networks ◽  
The One ◽  
Propagation Rule ◽  
Node Embeddings

AbstractGraph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. The layer-wise propagation rule of conventional GCN is designed in such a way that the feature aggregation at each node depends on the features of the one-hop neighbouring nodes. Adding an attention layer over the GCN can allow the network to provide different importance within various one-hop neighbours. These methods can capture the properties of static network, but is not well suited to capture the temporal patterns in time-varying networks. In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive pointwise mutual information matrix (PPMI) which denote the temporal correlation between the nodes. Furthermore, we design a TempGAN architecture which uses both adjacency and PPMI information to generate node embeddings from temporal network. Finally, we conduct link prediction experiments by designing a TempGAN autoencoder to evaluate the quality of the embedding generated, and the results are compared with other state-of-the-art methods.

scholarly journals A novel test for detecting gene-gene interactions in trio studies

2015 ◽  
Author(s):  
Brunilda Balliu ◽  
Noah Zaitlen
Keyword(s):  
Association Studies ◽  
Gene Interaction ◽  
Model Organisms ◽  
Gene Interactions ◽  
Complex Phenotypes ◽  
Multiple Hypothesis ◽  
Genome Wide ◽  
New Gene ◽  
Standard Models ◽  
Interaction Test

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new gene-gene interaction test for use in trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard one degree of freedom correlation test of interaction. We show via extensive simulations that our test substantially outperforms existing tests of interaction in trio studies. The gain in power under standard models of phenotype is large, with previous tests requiring more than twice the number of trios to obtain the power of our test. We also demonstrate a bias in a previous trio interaction test and identify its origin. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.

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