NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation

Yin, Meng-Meng; Liu, Jin-Xing; Gao, Ying-Lian; Kong, Xiang-Zhen; Zheng, Chun-Hou

doi:10.1109/tcyb.2020.3026652

Cited by 25 publications

(16 citation statements)

References 41 publications

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“…While many studies improve this issue, they still perform an SVD of Z in each iteration and make them disproportionate for managing dense and large-scale datasets. This paper indicates many nuclear norms regularized problems of the form (7) which can be optimized with a bilinear factorization of Z = UV T by using the variational definition of the nuclear norm. In this paper, a unification of traditional bilinear factorization and nuclear norm approaches under one formulation in DTI applications have been proposed.…”

Section: Discussionmentioning

confidence: 99%

“…In this regard, Computational Prediction (CP) methods have been used in recent years [6]. In addition, there is ample evidence of Disease-Associated Microbes (DAM) as well as Long non-coding RNA (lncRNA)-Disease Associations (LDA) [7,8]. Using traditional approaches of experiments to confirm these connections often requires a great deal of materials and time which are expected computational methods to be used to predict these associations.…”

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confidence: 99%

“…Using traditional approaches of experiments to confirm these connections often requires a great deal of materials and time which are expected computational methods to be used to predict these associations. Many of these algorithms use profile-based methods (for example, NCPLP [7] in ADM and BLM-NPAI [8] in LDA) to predict these associations.…”

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confidence: 99%

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Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

et al. 2021

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Background Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug–target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates. Results In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug–target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input. Conclusions The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

et al. 2021

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“… Peng et al (2020) developed a reliable negative sample selection method based on the random walk with restart and positive unlabeled learning, then used the logistic matrix factorization with neighborhood regularization for prediction. Yin et al (2020) also designed an integrated method using label propagation and network consistency projection. Some matrix factorization-based computational methods have been proposed to solve microbe–disease association prediction tasks or similar questions.…”

Section: Introductionmentioning

confidence: 99%

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes

Zhang

et al. 2022

Front. Microbiol.

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Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe–disease associations (MDAs) can help reveal the pathological mechanism of human diseases and be useful for the prevention, diagnosis, and treatment of human diseases. Therefore, it is necessary to develop effective computational models and reduce the cost and time of biological experiments. Here, we developed a novel machine learning-based joint framework called CWNMF-GLapRLS for human MDA prediction using the proposed collaborative weighted non-negative matrix factorization (CWNMF) technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with missing values and effectively overcome the data sparsity problem, we proposed a collaborative weighted NMF technique to reconstruct the original association matrix. In addition, we developed a graph Laplacian regularized least-squares method for prediction. The experimental results of fivefold and leave-one-out cross-validation demonstrated that our method achieved the best performance by comparing it with 5 state-of-the-art methods on the benchmark dataset. Case studies further showed that the proposed method is an effective tool to predict potential MDAs and can provide more help for biomedical researchers.

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“…The model first applied a random walk algorithm to fuse various similarity networks and then adopted bidirectional label propagation to make predictions. Yin et al (2020) created the NCPLP model based on network consistency projection and label propagation to predict microbe-disease interactions. These biological network-based methods provide a fresh perspective and framework with which we can construct new computational models.…”

Section: Introductionmentioning

confidence: 99%

NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk

et al. 2022

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Long non-coding RNAs (lncRNAs) play significant roles in the disease process. Understanding the pathological mechanisms of lncRNAs during the course of various diseases will help clinicians prevent and treat diseases. With the emergence of high-throughput techniques, many biological experiments have been developed to study lncRNA-disease associations. Because experimental methods are costly, slow, and laborious, a growing number of computational models have emerged. Here, we present a new approach using network consistency projection and bi-random walk (NCP-BiRW) to infer hidden lncRNA-disease associations. First, integrated similarity networks for lncRNAs and diseases were constructed by merging similarity information. Subsequently, network consistency projection was applied to calculate space projection scores for lncRNAs and diseases, which were then introduced into a bi-random walk method for association prediction. To test model performance, we employed 5- and 10-fold cross-validation, with the area under the receiver operating characteristic curve as the evaluation indicator. The computational results showed that our method outperformed the other five advanced algorithms. In addition, the novel method was applied to another dataset in the Mammalian ncRNA-Disease Repository (MNDR) database and showed excellent performance. Finally, case studies were carried out on atherosclerosis and leukemia to confirm the effectiveness of our method in practice. In conclusion, we could infer lncRNA-disease associations using the NCP-BiRW model, which may benefit biomedical studies in the future.

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NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation

Cited by 25 publications

References 41 publications

Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

Drug–target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes

NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk

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