Manifold regularized matrix factorization for drug-drug interaction prediction

Zhang, Wen; Chen, Yanlin; Li, Dingfang; Yue, Xiang

doi:10.1016/j.jbi.2018.11.005

Cited by 106 publications

(58 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Instead of relying on the complicated architecture of the deep learning models, more transparent approaches have been developed to represent the networks numerically for further analysis. Early attempts at applying the graph embedding-based approaches include predicting potential interactions in various biomedical networks ( Wang et al., 2017b ; Zhang et al., 2018 ), predicting protein functions ( Cho et al., 2016 ), and detecting differential pathways in scRNA-seq data ( Costa et al., 2018 ).…”

Section: Omics Technologies and Computational Methodologies For Netwomentioning

confidence: 99%

Network Approaches for Dissecting the Immune System

et al. 2020

View full text Add to dashboard Cite

The immune system is a complex biological network composed of hierarchically organized genes, proteins, and cellular components that combat external pathogens and monitor the onset of internal disease. To meet and ultimately defeat these challenges, the immune system orchestrates an exquisitely complex interplay of numerous cells, often with highly specialized functions, in a tissue-specific manner. One of the major methodologies of systems immunology is to measure quantitatively the components and interaction levels in the immunologic networks to construct a computational network and predict the response of the components to perturbations. The recent advances in high-throughput sequencing techniques have provided us with a powerful approach to dissecting the complexity of the immune system. Here we summarize the latest progress in integrating omics data and network approaches to construct networks and to infer the underlying signaling and transcriptional landscape, as well as cell-cell communication, in the immune system, with a focus on hematopoiesis, adaptive immunity, and tumor immunology. Understanding the network regulation of immune cells has provided new insights into immune homeostasis and disease, with important therapeutic implications for inflammation, cancer, and other immune-mediated disorders.

show abstract

Section: Omics Technologies and Computational Methodologies For Netwomentioning

confidence: 99%

Network Approaches for Dissecting the Immune System

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Nonnegative matrix factorization (NMF) is a significant algorithm which can be applied in recommender system for data representation [32]. Recent years, NMF has been successfully utilized to predict potential interactions of drug-drug [41], lncRNA-protein [42], miRNA-disease [43], Microbe-disease [44], drug-disease [45], CircRNA-disease [46], etc.. NMF aims to obtain lncRNA-miRNA interaction score matrix by decomposing the original adjacency matrix into two low-dimensional nonnegative matrices directly [38]. In this work, the lncRNA-miRNA interaction adjacency matrix Y ∈ R 468×262 is decomposed into U ∈ R k×468 and V ∈ R k×262 , k is the sub-space dimensionality (k < rn/(r + n)).…”

Section: B Related Work 1) the Standard Nonnegative Matrix Factorizamentioning

confidence: 99%

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Wang

You

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in the regulation of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interaction computational prediction approach urgently need to be developed. In this work, we proposed a novel computational model, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately infer potential interactions between lncRNAs and miRNAs. To assess the performance of GNMFLMI, five-fold cross-validation experiments are carried out. The AUC values achieved by GNMFLMI on two datasets are 0.9769 and 0.8894, respectively, which outperform the compared methods. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research.

show abstract

“…• Drug-Drug interaction prediction: The drug-drug relationship is detected through a symmetrical drugdrug matrix/network [83]. This helps predict drugs similar to the ones known to be effective against a pathogen/disease.…”

Section: Modeling Biological Interactionsmentioning

confidence: 99%

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19

Majumdar¹,

Mongia²,

Chouzenoux³

et al. 2020

Preprint

View full text Add to dashboard Cite

This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns. The input matrix is partially filled, with ones in positions where the antiviral has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) is encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results on our curated RNA drug virus association (DVA) dataset shows that the proposed approach excels over state-of-the-art graph regularized matrix completion techniques. When applied to in silico prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under for trials for the same.<br>

show abstract

Manifold regularized matrix factorization for drug-drug interaction prediction

Cited by 106 publications

References 43 publications

Network Approaches for Dissecting the Immune System

Network Approaches for Dissecting the Immune System

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19

Contact Info

Product

Resources

About