Non-negative Matrix Tri-Factorization for Data Integration and Network-based Drug Repositioning

Ceddia, Gaia; Pinoli, Pietro; Ceri, Stefano; Masseroli, Marco

doi:10.1109/cibcb.2019.8791474

Cited by 8 publications

(3 citation statements)

References 30 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…random walk (RW) approach; such as RWHNDR ( Luo et al , 2018a ), TL-HGBI ( Wang et al , 2014b ), MBiRW ( Luo et al , 2016 ); and matrix factorization (MF) approach; such as DRRS ( Luo et al , 2018a ), KBMF ( Gönen et al , 2013 ), MSBMF ( Yang et al , 2021 ), SCPMF( Meng et al , 2021 ). RW approach is more scalable and popular, but MF approach achieves higher accuracy ( Luo et al , 2018a ).There are also other MF-based methods which instead of drug-disease networks, they use drug-protein network for DR purpose such as Ceddia et al (2020) , Ceddia et al (2019) and Dissez et al (2019) .…”

Section: Introductionmentioning

confidence: 99%

A network-based drug repurposing method via non-negative matrix factorization

2021

View full text Add to dashboard Cite

Motivation Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This paper presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. Results The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction. Availability The program is available at https://github.com/sshaghayeghs/NMF-DR

show abstract

Section: Introductionmentioning

confidence: 99%

A network-based drug repurposing method via non-negative matrix factorization

2021

View full text Add to dashboard Cite

show abstract

“…Its mainstream algorithms include: network inference, [9][10][11][12] random walk, 13,14 and matrix factorization. 15,16 These methods usually rely heavily on the richness of interaction network data. In addition, many studies that used such methods proposed the need to construct more complete and large-scale data information to improve prediction performance.…”

Section: Introductionmentioning

confidence: 99%

HeTDR: Drug repositioning based on heterogeneous networks and text mining

Jin

Niu²,

Jiang

et al. 2021

Patterns

View full text Add to dashboard Cite

show abstract

“…Most machine learning-based methods often depend on typical classifiers trained by a collection of features and known drug–disease associations, such as the Support Vector Machine (SVM), the Random Forest (RF), and Lasso Regression (LR), which do not achieve the desired performance. ,, The literature mining methods depend on the term co-occurrences and semantic inference of keywords of interest to infer new drug–disease associations. , Due to the ambiguity of the nature of natural language and the limited accuracy of text mining techniques, it is difficult for literature mining-based methods to acquire reliable predictions . Network-based methods attempt to find new indications for approved drugs using topological characteristics of drug–disease networks to find associations linking pathogenesis and symptoms to known drugs. − …”

Section: Introductionmentioning

confidence: 99%

HNet-DNN: Inferring New Drug–Disease Associations with Deep Neural Network Based on Heterogeneous Network Features

Liu

Zhang

Song

et al. 2020

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, referred to as drug repositioning, which provides an economical and efficient way for drug discovery. With increasing volumes of large-scale chemical, genomic, and pharmacological data sets generated by the high-throughput technique, it is crucial to develop systematic and rational computational approaches to identify new indications of approved drugs. In this paper, we introduce HNet-DNN, which utilizes a deep neural network (DNN), to predict new drug–disease associations based on the features extracted from the drug–disease heterogeneous network. Instead of the straightforward concatenation of chemical and phenotypic features as the input of DNN, we used these raw features of drugs and diseases to construct a drug–drug similarity network and a disease–disease similarity network, and then built a drug–disease heterogeneous network by integrating known drug–disease associations. Subsequently, we extracted topological features for drug–disease associations from the heterogeneous network and used them to train a DNN model. Our intensive performance evaluations demonstrated that HNet-DNN effectively exploits the features of the heterogeneous network to boost the predictive performance of drug–disease associations. Compared with a couple of typical classifiers and competitive approaches, our method not only achieved state-of-the-art performance but also effectively alleviated the overfitting problem. Moreover, we ran HNet-DNN to predict new drug–disease associations and carried out case studies to verify the effectiveness of our method.

show abstract

Non-negative Matrix Tri-Factorization for Data Integration and Network-based Drug Repositioning

Cited by 8 publications

References 30 publications

A network-based drug repurposing method via non-negative matrix factorization

A network-based drug repurposing method via non-negative matrix factorization

HeTDR: Drug repositioning based on heterogeneous networks and text mining

HNet-DNN: Inferring New Drug–Disease Associations with Deep Neural Network Based on Heterogeneous Network Features

Contact Info

Product

Resources

About