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DDI Prediction via Heterogeneous Graph Attention Networks [article]

Farhan Tanvir, Khaled Mohammed Saifuddin, Esra Akbas
2022 arXiv   pre-print
We create a heterogeneous network of drugs with different biological entities. Then, we develop a heterogeneous graph attention network to learn DDIs using relations of drugs with other entities.  ...  As a result, computational methods are needed for predicting DDIs. In this paper, we present a novel heterogeneous graph attention model, HAN-DDI to predict drug-drug interactions.  ...  This graph convolution network model was created to predict multi-relational links in heterogeneous networks.  ... 
arXiv:2207.05672v1 fatcat:6qkqmswyvffh7mjm3klnctmidi

Scalable and Accurate Drug–target Prediction Based on Heterogeneous Bio-linked Network Mining [article]

Nansu Zong, Rachael Sze Nga Wong, Victoria Ngo, Yue Yu, Ning Li
2019 bioRxiv   pre-print
Results: We introduce a drug target prediction method that improved our previously proposed method from the three aspects: 1) we constructed a heterogeneous network which incorporates 12 repositories and  ...  New drug-target associations were successfully predicted with AUC ROC in average, 97.2% (validated based on the DrugBank 5.1.0).  ...  Data preparation and benchmarking This study utilized information of the various biological entities that related to drugs and targets to form a multipartite network called Linked Multipartite Network  ... 
doi:10.1101/539643 fatcat:d3na6x4x2naafmap4rqyvlfvne

Systems Biology–Derived Genetic Signatures of Mastitis in Dairy Cattle: A New Avenue for Drug Repurposing

Somayeh Sharifi, Maryam Lotfi Shahreza, Abbas Pakdel, James M. Reecy, Nasser Ghadiri, Hadi Atashi, Mahmood Motamedi, Esmaeil Ebrahimie
2021 Animals  
Online data repositories relevant to known diseases, drugs, and gene targets, along with other specialized biological information for E. coli mastitis, including critical genes with robust bio-signatures  ...  Unraveling the bio-signature of mastitis in dairy cattle can open new avenues for drug repurposing.  ...  protein targets, and ability to use heterogeneous data [21, 22] .  ... 
doi:10.3390/ani12010029 pmid:35011134 pmcid:PMC8749881 fatcat:3s5octj43jaclfzyxzfyrnwooe

Embedding Logical Queries on Knowledge Graphs [article]

William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec
2019 arXiv   pre-print
We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions  ...  For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?"  ...  Drug-protein links describe the proteins targeted by a given drug.  ... 
arXiv:1806.01445v4 fatcat:s6qwg3sosrflnbqb372v45ha6e

Neuro-symbolic representation learning on biological knowledge graphs

Mona Alshahrani, Mohammad Asif Khan, Omar Maddouri, Akira R Kinjo, Núria Queralt-Rosinach, Robert Hoehndorf, Janet Kelso
2017 Bioinformatics  
We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target  ...  Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information  ...  We train a logistic regression classifier to predict whether a pair of drugs (represented by the embeddings we generate) share an indication or target.  ... 
doi:10.1093/bioinformatics/btx275 pmid:28449114 pmcid:PMC5860058 fatcat:6henlhfbgvee3m74izxt4rgcmq

Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation

Chia-Ter Chao, You-Tien Tsai, Wen-Ting Lee, Hsiang-Yuan Yeh, Chih-Kang Chiang, Vladimir Jakovljevic
2022 Oxidative Medicine and Cellular Longevity  
heterogeneous network using the graph neural network architecture and a random forest classifier established for prediction.  ...  end-to-end transformer network and a node2vec algorithm and global embedding vectors learned from heterogeneous network via the graph neural network.  ...  of the interaction network for drug-disease interaction prediction.  ... 
doi:10.1155/2022/4378413 pmid:35035662 pmcid:PMC8754599 fatcat:nhzleduxwzchxlgd5dxt746xym

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition

Mina Gachloo, Yuxing Wang, Jingbo Xia
2019 Genomics & Informatics  
Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery.  ...  , targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level.  ...  This study used Online Predicted Human Interaction Database (OPHID), a predicted protein association network database, to obtain protein networks of Alzheimer disease, retrieved from disease-drug-protein  ... 
doi:10.5808/gi.2019.17.2.e18 pmid:31307133 pmcid:PMC6808632 fatcat:w65sf7kkevflneg4marbtnmw6m

Application and evaluation of knowledge graph embeddings in biomedical data

Mona Alshahrani, Maha A. Thafar, Magbubah Essack
2021 PeerJ Computer Science  
However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, "knowledge graphs".  ...  The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities  ...  Graph convolutional networks-based embedding methods Recently, graph convolutional networks (GCNs) have also been utilized to learn KG embeddings for several tasks, including link prediction and entity  ... 
doi:10.7717/peerj-cs.341 pmid:33816992 pmcid:PMC7959619 fatcat:3f5kmbwalzbq7l6xcixwdxa6fa

Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery [article]

Xiaoqi Wang, Yingjie Cheng, Yaning Yang, Fei Li, Shaoliang Peng
2022 arXiv   pre-print
We design six basic SSL tasks that are inspired by various modality features including structures, semantics, and attributes in biomedical heterogeneous networks.  ...  Therefore, we propose multi-task joint strategies of self-supervised representation learning on biomedical networks for drug discovery, named MSSL2drug.  ...  Simultaneously, DDI and DTI predictions are treated as link predictions in homogeneous and heterogeneous networks, respectively.  ... 
arXiv:2201.04437v1 fatcat:dklnrwpvlzhn7jhuxxg3dj7suy

Survey on graph embeddings and their applications to machine learning problems on graphs

Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj
2021 PeerJ Computer Science  
in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization.  ...  Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization,  ...  Some articles (Zitnik & Zupan, 2016; Zitnik, Agrawal & Leskovec, 2018) focus on similar drug-drug and drug-target interaction prediction.  ... 
doi:10.7717/peerj-cs.357 pmid:33817007 pmcid:PMC7959646 fatcat:ntronyrbgfbedez5dks6h4hoq4

Biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data [article]

Magdalena Wysocka, Oskar Wysocki, Marie Zufferey, Dónal Landers, André Freitas
2022 arXiv   pre-print
networks) and interpretability.  ...  We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction  ...  pathways; drug single-omic: T + drug drug response sensitivity; KEGG, GDSC, [39] 2020 targets + drug ARCH sparse DNN pathway node activity prediction drug-protein STITCH sensitivity interactions pathways  ... 
arXiv:2207.00812v1 fatcat:zgjd3i7gazf5tpzud2io54tbp4

Learning to Discover Medicines [article]

Tri Minh Nguyen, Thin Nguyen, Truyen Tran
2022 arXiv   pre-print
reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning  ...  We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven  ...  Given a pair of drugs, the drugs are embedded using the encoder and the polypharmacy prediction task is formulated as a link prediction task.  ... 
arXiv:2202.07096v1 fatcat:u77zls6hezffbkmm3zy2rhnueu

A Survey on Drug-Target Interaction Prediction Methods Analysis of Prediction Mechanisms for Drug Target Discovery

Shyama M Nair
2018 International Journal for Research in Applied Science and Engineering Technology  
Prediction of drug-target interactions can be done by experimentally and it is very expensive and time consuming.  ...  Therefore, there is a continuous demand for effective and low-cost computational techniques for drug target interaction prediction.  ...  [21] proposed multi view low rank embedding to predict drug target interactions. Here heterogeneous data sources are used to predict drug-target interactions.  ... 
doi:10.22214/ijraset.2018.3057 fatcat:s3qchrwn5fbv3e3bkt7nfjtaum

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Hyunho Kim, Eunyoung Kim, Ingoo Lee, Bongsung Bae, Minsu Park, Hojung Nam
2020 Biotechnology and Bioprocess Engineering  
This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization  ...  , and drug repositioning.  ...  Acknowledgments This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2020R1A2C2004628), and was supported by the Bio-Synergy Research Project  ... 
doi:10.1007/s12257-020-0049-y pmid:33437151 pmcid:PMC7790479 fatcat:wqdmkkas2nb65gy3pymlgisuwi

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [article]

Xiao Wang and Deyu Bo and Chuan Shi and Shaohua Fan and Yanfang Ye and Philip S. Yu
2020 arXiv   pre-print
space while preserving the heterogeneous structures and semantics for downstream tasks (e.g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent  ...  Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension  ...  Prediction layer utilizes the labeled nodes to update the node embeddings. It consists of three parts: node-level attention, semanticlevel attention and prediction.  ... 
arXiv:2011.14867v1 fatcat:phfoxj7qsrfshfednomeok7pau
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