MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

Liu, Haiyan; Bing, Pingping; Zhang, Meijun; Tian, Geng; Ma, Jun; Li, Haigang; Bao, Meihua; He, Kai; He, Jianjun; He, Binsheng; Yang, Jialiang

doi:10.1016/j.csbj.2022.12.053

Cited by 10 publications

(7 citation statements)

References 76 publications

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“…To evaluate the MDA prediction performance of our proposed GPUDMDA method, we compared it with other MDA identification methods (LRLSHMDA, NTSHMDA, GATMDA, and MNMDA) under five-fold cross validation (CV) on diseases, microbes, and microbe–disease pairs for 20 times. LRLSHMDA (Wang et al, 2017 ) is Laplacian regularized least square-based MDA identification algorithm, NTSHMDA (Luo and Long, 2018 ) is integrated random walk and network topology similarity, GATMDA (Long et al, 2021 ) combined inductive matrix completion and graph attention networks to complete missing MDAs, and MNNMDA (Liu et al, 2023 ) used a low-rank matrix completion model for identifying possible MDAs. During MDA prediction, it is not enough to reflect the MDA identification performance of a computational model only through cross-validation on microbe–disease pairs.…”

Section: Resultsmentioning

confidence: 99%

“…Network-based algorithms take MDA prediction as a random walk or label propagation problem. For example, to decode underlying MDAs, BRWMDA fused similarity networks and bi-random walk (Yan et al, 2019 ), NBLPIHMDA developed a bidirectional label propagation algorithm (Wang et al, 2019 ), MHEN constructed a multiplex heterogeneous network (Ma and Jiang, 2020 ), WMGHMDA implemented iteratively weighted meta-graph search model (Long and Luo, 2019 ), RWHMDA was a hypergraph-based random walk method (Niu et al, 2019 ), BDHNS formulated a bi-directional heterogeneous MDA network (Guan et al, 2022 ), and MNNMDA used low-rank matrix completion (Liu et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

Peng,

Huang,

Tian

et al. 2023

Front. Microbiol.

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BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

Peng,

Huang,

Tian

et al. 2023

Front. Microbiol.

Self Cite

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“…Here, considering the limited availability of microbial drug association prediction methods, we would first compare NMGMDA with some representative methods for link prediction problems such as HMDAKATZ 14 , HMDA-Pred 30 , LAGCN 31 , MNNMAD 32 and GSAMDA 20 , etc. One of them, HMDAKATZ, predicted the association between microbes and drugs using the KATZ algorithm as a foundation.…”

Section: Comparison With Advanced Methodsmentioning

confidence: 99%

NMGMDA: A Computational Model for Predicting Potential Microbe–Drug Associations based on Minimize Matrix Nuclear Norm and Graph Attention Network

Liang,

Liu,

Chen

et al. 2023

Preprint

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For drug research and development, the probable microbe-drug associations can be predicted with considerable utility. Deep learning-based techniques have recently found widespread use in the biomedical industry and have significantly improved identification performance. Additionally, the growing body of knowledge on germs and pharmaceutical biomedicine offers a fantastic potential for methods based on deep learning to forecast hidden associations between microbes and drugs. In order to infer latent microbe-drug associations, we developed a unique computational model in this publication called NMGMDA based on the nuclear norm minimization and graph attention network. We created a heterogeneous microbe-drug network in NMGMDA by fusing the drug and microbe similarities with the established associations between drugs and microbes. Then, in order to get predicted scores of potential microbe-drug associations, we used the nuclear norm minimization approach and a GAT-based auto-encoder, respectively. The final results, which are based on two datasets and weighted average of these two predicted scores, demonstrated that NMGMDA can outperform state-of-the-art competitive approaches. Case studies further demonstrated its capacity to reliably find fresh associations.

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“…Researchers are developing learning paradigms such as meta-learning categorized as metric (similarity based on distance metrics), model (internal and external memory) and optimization (optimizing model parameters for fast learning). Meta-learning helps to solve data scarcity problems in disease diagnosis ( Liu H et al, 2023 ). Multi-diagnosis methods are based on the three dimensions of meta-learning.…”

Section: Multi-omics Data Integration Interpretation and Disease Pred...mentioning

confidence: 99%

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Wekesa

Kimwele

2023

Front. Genet.

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Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

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MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

Cited by 10 publications

References 76 publications

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

NMGMDA: A Computational Model for Predicting Potential Microbe–Drug Associations based on Minimize Matrix Nuclear Norm and Graph Attention Network

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

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