A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
[article]
2018
bioRxiv
pre-print
Preserved Fulltext
of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns ...
In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound protein binding affinity data). ...
We acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. ...
doi:10.1101/261396
fatcat:ziaiqumn2nhylgyvdq3ivs5vhe
NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
2018
Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
many network-related prediction tasks, we develop a new nonlinear endto-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns ...
In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). ...
We acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. ...
doi:10.1093/bioinformatics/bty543
fatcat:4pmchscvrzhjjj5iid3mbz7bkq
HGDTI: predicting drug–target interaction by using information aggregation based on heterogeneous graph neural network
2022
BMC Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Conclusions The HGDTI based on heterogeneous graph neural network model, can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development ...
These results indicate that HGDTI can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. ...
This inspired us to build our own model for discovering new DTIs. In this paper, we present HGDTI model, a heterogeneous graph neural network for predicting DTI. ...
doi:10.1186/s12859-022-04655-5
pmid:35413800
pmcid:PMC9004085
fatcat:2dyl3dw2irf6ng4zmqkmoojjgu
GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network
2021
Frontiers in Genetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this article, a graph autoencoder approach for DTI prediction (GADTI) was proposed to discover potential interactions between drugs and targets using a heterogeneous network, which integrates diverse ...
Identifying drug–target interaction (DTI) is the basis for drug development. However, the method of using biochemical experiments to discover drug-target interactions has low coverage and high costs. ...
GADTI first constructs a heterogeneous network that integrates eight data sources related to drugs and targets. Then, it runs a graph autoencoder model on the network to discover new DTIs. ...
doi:10.3389/fgene.2021.650821
pmid:33912218
pmcid:PMC8072283
fatcat:lohwdhopivdonnransj5ktbgqe
MGATRx: Discovering Drug Repositioning Candidates Using Multi-view Graph Attention
[article]
2020
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
By integrating drug-centric and disease-centric annotations as multi-views, we propose a multi-view graph attention network for indication discovery (MGATRx). ...
In-silico drug repositioning or predicting new indications for approved or late-stage clinical trial drugs is a resourceful and time-eficient strategy in drug discovery. ...
[3] created hetionet a heterogeneous network integrating data from 29 public resources to identify drug repositioning candidates and predict the probability of treatment for drug-disease pairs [4] ...
doi:10.1101/2020.06.29.171876
fatcat:iqatueqpwvacjdmzn2l7ygjjo4
An improved graph representation learning method for drug-target interaction prediction over heterogeneous biological information graph
[article]
2022
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Computational-based strategies for predicting drug-target interactions (DTIs) are regarded as a high-efficiency way. ...
The prediction task of the relationships between drugs and targets plays a significant role in the process of new drug discovery. ...
Wan et. al [12] developed a nonlinear end-to-end learning model, namely NeoDTI, by integrating multiple information from heterogeneous network data to learn the network representations of drugs and targets ...
doi:10.1101/2022.06.30.498357
fatcat:m4wqkfkdfbbedebjitarxdrw7i
Machine Learning for Drug-Target Interaction Prediction
2018
Molecules
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. ...
Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction ...
Acknowledgments: We would like to thank all authors of the cited references.
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/molecules23092208
pmid:30200333
pmcid:PMC6225477
fatcat:gcijck47irhqvlttntb43r63fu
Deep learning integration of molecular and interactome data for protein-compound interaction prediction
[article]
2021
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
However, few attempts have been made to combine both types of data in molecular information and interaction networks. ...
Results: We developed a deep learning-based method that integrates protein features, compound features, and heterogeneous interactome data to predict protein-compound interactions. ...
Nat Commun 595 Hong L, Xiao A, Jiang T, Zeng J (2019) NeoDTI: neural integration of 597 neighbor information from a heterogeneous network for discovering new drug-target 598 interactions. ...
doi:10.1101/2021.01.31.429000
fatcat:wzx6ukgt6naupabjosx32hemca
Deep learning for drug repurposing: methods, databases, and applications
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious ...
However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other ...
[100] NeoDTI integrated neighborhood information of nodes in the heterogeneous network and automatically learned topology-preserving representations of drugs and targets. ...
arXiv:2202.05145v1
fatcat:5oqujy2daffdpa33b4cbrg6hqy
Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The discovery of drug-target interactions (DTIs) is a very promising area of research with great potential. ...
In particular, we take a multi-layered network perspective, where different layers correspond to different similarity metrics between drugs and targets. ...
In Luo dataset, the neighbor drugs are more likely to share the same interactions, leading to the effectiveness of neighborhood-based interaction estimation for new drugs. ...
arXiv:2201.09508v1
fatcat:nvbbtymxbba6bigjicz47au6um
Drug-Disease Association Prediction Based on Neighborhood Information Aggregation in Neural Networks
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Computational drug repositioning plays a vital role in the prediction of drug function. Many new functions discovered have been confirmed. ...
It is based on neighborhood information aggregation in neural networks which combines the similarity of diseases and drugs, the associations between the drugs and diseases. ...
NeoDTI [23] predicts new drugs and drug targets by integrating various information in heterogeneous networks and conducting end-to-end learning through a nonlinear model. ...
doi:10.1109/access.2019.2907522
fatcat:3i5mo3gjkrbzvflcedsqkov7le
Network approaches for modeling the effect of drugs and diseases
2022
Briefings in Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for ...
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. ...
Graph neural network-based methods NeoDTI Deep learning, graph Drug-target DTIs, drug-drug • Predicts drug-target interactions by Computational cost of convolutional network interactions, using several ...
doi:10.1093/bib/bbac229
pmid:35704883
pmcid:PMC9294412
fatcat:wbmt5bm7mbeazjc56s6fvvap3q
Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Both three proposed approaches incorporate a novel local interaction consistency aware similarity interaction method to generate fused drug and target similarities that preserve vital information from ...
In drug discovery, identifying drug-target interactions (DTIs) via experimental approaches is a tedious and expensive procedure. ...
ACKNOWLEDGMENTS Bin Liu was supported from the China Scholarship Council (CSC) under the Grant CSC No.201708500095. ...
arXiv:2105.01545v2
fatcat:ja2fcyiyajc2bgj46bridbhuw4
Target Identification among Known Drugs by Deep Learning from Heterogeneous Networks
2020
Chemical Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Without foreknowledge of the complete drug target information, development of promising and affordable approaches for effective treatment of human diseases is challenging. ...
Here, we develop deepDTnet, a deep learning methodology... ...
Subsequently, the same group further proposed, NeoDTI, 19 a neural network-based approach, for DTI prediction with an improved performance. ...
doi:10.1039/c9sc04336e
pmid:34123272
pmcid:PMC8150105
fatcat:kn7s6effine65bxx6oxq4rucxy
Graph Representation Learning in Biomedicine
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge. ...
We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces. ...
M.M.L. is supported by T HG from the National Human Genome Research Institute and a National Science Foundation Graduate Research Fellowship. ...
arXiv:2104.04883v3
fatcat:lrhxlztborbylazvdfmaxk5zem
« Previous
Showing results 1 — 15 out of 18 results