Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction

Ma, Yingjun; Liu, Qingquan

doi:10.1016/j.compbiomed.2022.105503

Cited by 18 publications

(11 citation statements)

References 53 publications

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“…One of the most prevalent algorithms in recommendation systems is matrix decomposition ( Ma and Liu, 2022 ). As a standard recommendation system for collaborative filtering based on SVD ( Vozalis and Margaritis, 2007 ), the idea of SVD is to transform an arbitrary matrix

into A =

by a set of orthogonal basis transformations.…”

Section: Methodsmentioning

confidence: 99%

MDSVDNV: predicting microbe–drug associations by singular value decomposition and Node2vec

Tan,

Zhang,

Liu

et al. 2024

Front. Microbiol.

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IntroductionRecent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes.MethodsIn this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions.Results and discussionCompared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe–drug associations in the future.

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into A =

by a set of orthogonal basis transformations.…”

Section: Methodsmentioning

confidence: 99%

MDSVDNV: predicting microbe–drug associations by singular value decomposition and Node2vec

Tan,

Zhang,

Liu

et al. 2024

Front. Microbiol.

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“…Generalized Matrix Factorization (GMF) ( Lee and Seung 2000 , Shan and Banerjee 2010 , Ma and Liu 2022 ) is widely used in collaborative filtering for recommendation. Generally, the input of the model is a one-hot encoded representation, which is fed into one fully connected layer to generate the dense vector of circRNAs or diseases.…”

Section: Methodsmentioning

confidence: 99%

MLNGCF: circRNA–disease associations prediction with multilayer attention neural graph-based collaborative filtering

Deng

Zhang

et al. 2023

Bioinformatics

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Motivation CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area of research. Due to the high cost of validating circRNA-disease associations using traditional wet-lab experiments, novel computational methods based on machine learning are gaining more and more attention in this field. However, current computational methods suffer to insufficient consideration of latent features in circRNA-disease interactions. Results In this study, a multi-layer attention neural graph based collaborative filtering (MLNGCF) is proposed. MLNGCF first enhances multiple biological information with autoencoder as the initial features of circRNAs and diseases. Then, by constructing a central network of different diseases and circRNAs, a multi-layer cooperative attention-based message propagation is performed on the central network to obtain the high-order features of circRNAs and diseases. A neural network based collaborative filtering is constructed to predict the unknown circRNA-disease associations and update the model parameters. Experiments on the benchmark datasets demonstrate that MLNGCF outperforms state-of-the-art methods, and the prediction results are supported by the literature in the case studies. Availability The source codes and benchmark datasets of MLNGCF are available at https://github.com/ABard0/MLNGCF. Supplementary information Supplementary data are available at Bioinformatics online.

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“…EMPHCN [34] is a drug repositioning method, which is based on enhanced message passing and hypergraph convolutional networks to predict DDiIs. WHGMF [35] uses the generalized matrix factorization based on a weighted hypergraph learning model for microbialdrug association predictions. HHDTI [36] is a heterogeneous hypergraph-based framework…”

Section: Hypergraphmentioning

confidence: 99%

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks

Jin,

Hong,

Zeng

et al. 2023

PLoS Comput Biol

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The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.

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Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction

Cited by 18 publications

References 53 publications

MDSVDNV: predicting microbe–drug associations by singular value decomposition and Node2vec

MDSVDNV: predicting microbe–drug associations by singular value decomposition and Node2vec

MLNGCF: circRNA–disease associations prediction with multilayer attention neural graph-based collaborative filtering

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks

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