Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies

2018 ◽  
Vol 83 (2) ◽  
pp. 79-91
Author(s):  
Jeanette Prinz ◽  
Mohamad Koohi-Moghadam ◽  
Hongzhe Sun ◽  
Jean-Pierre A. Kocher ◽  
Junwen Wang
Keyword(s):  
Neural Network ◽  
Side Effects ◽  
Drug Target ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Drug Side Effects ◽  
Network Approach ◽  
Neural Network Approach ◽  
Genome Wide

scholarly journals Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Haohan Wang ◽  
Tianwei Yue ◽  
Jingkang Yang ◽  
Wei Wu ◽  
Eric P. Xing
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complex Traits ◽  
Population Stratification ◽  
Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. Results In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. Conclusions After validating the performance of our method using simulation experiments, we further apply it to Alzheimer’s disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer’s disease.

scholarly journals The druggable genome and support for target identification and validation in drug development

2016 ◽  
Author(s):  
Chris Finan ◽  
Anna Gaulton ◽  
Felix Kruger ◽  
Tom Lumbers ◽  
Tina Shah ◽  
...  
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Complex Disease ◽  
Target Identification ◽  
Association Studies ◽  
Target Selection ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Druggable Genome

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

scholarly journals Drug Targetor: a web interface to investigate the human druggome for over 500 phenotypes

2018 ◽  
Vol 35 (14) ◽  
pp. 2515-2517 ◽  
Author(s):  
Héléna A Gaspar ◽  
Christopher Hübel ◽  
Gerome Breen
Keyword(s):  
Web Application ◽  
Drug Target ◽  
Association Studies ◽  
Source Code ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Web Interface ◽  
Genome Wide ◽  
Therapeutic Mechanisms

Abstract Summary Results from hundreds of genome-wide association studies (GWAS) are now freely available and offer a catalogue of the association between phenotypes across medicine with variants in the genome. With the aim of using this data to better understand therapeutic mechanisms, we have developed Drug Targetor, a web interface that allows the generation and exploration of drug–target networks of hundreds of phenotypes using GWAS data. Drug Targetor networks consist of drug and target nodes ordered by genetic association and connected by drug–target or drug–gene relationship. We show that Drug Targetor can help prioritize drugs, targets and drug–target interactions for a specific phenotype based on genetic evidence. Availability and implementation Drug Targetor v1.21 is a web application freely available online at drugtargetor.com and under MIT licence. The source code can be found at https://github.com/hagax8/drugtargetor. Supplementary information Supplementary data are available at Bioinformatics online.

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