RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation

Yang, Cheng; Huang, Li; Chen, Xiang; Fu, Xiquan; Liu, Wei

doi:10.1093/bib/bbac155

Cited by 47 publications

(23 citation statements)

References 69 publications

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“…As in ( Peng et al, 2017b ) for example, a matrix recovery approach was utilized to integrate the weight matrix to recover association matrix; hence novel latent associations were accurately inferred without the need for negative samples. Integrated with the label propagation algorithm, a study in ( Peng et al, 2022 ) adopted robust nonnegative matrix factorization to predict underlying associations more precisely. To be specific, using the integrated similarity information, the original adjacency matrix was updated via matrix multiplication to reduce the influence of negative samples.…”

Section: Predicting Mirna-disease Associationsmentioning

confidence: 99%

Machine learning in the development of targeting microRNAs in human disease

et al. 2023

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A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.

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Section: Predicting Mirna-disease Associationsmentioning

confidence: 99%

Machine learning in the development of targeting microRNAs in human disease

et al. 2023

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“…Luo et al apply NMF to predict disease-related biomarkers (genes) [124]. Lin and Ma predict disease-related biomarkers (lncRNA) in heterogeneous networks with co-regularized NMF [125]. Peng et al develop Rnmflp for predicting disease-related biomarkers (circRNAs) based on robust NMF and label propagation [126].…”

Section: A Uv T a Uv Tmentioning

confidence: 99%

Network Learning for Biomarker Discovery

Ding¹,

Fu²,

Luo³

et al. 2023

IJNDI

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Survey/review study Network Learning for Biomarker Discovery Yulian Ding 1, Minghan Fu 1, Ping Luo 2, and Fang-Xiang Wu 1,3,4,* 1 Division of Biomedical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 2 Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada 3 Department of Computer Sciences, University of Saskatchewan, S7N 5A9, Saskatoon, Canada 4 Department of Mechanical Engineering, University of Saskatchewan, S7N 5A9, Saskatoon, Canada * Correspondence: faw341@mail.usask.ca Received: 14 October 2022 Accepted: 5 December 2022 Published: Abstract: Everything is connected and thus networks are instrumental in not only modeling complex systems with many components, but also accommodating knowledge about their components. Broadly speaking, network learning is an emerging area of machine learning to discover knowledge within networks. Although networks have permeated all subjects of sciences, in this study we mainly focus on network learning for biomarker discovery. We first overview methods for traditional network learning which learn knowledge from networks with centrality analysis. Then, we summarize the network deep learning, which are powerful machine learning models that integrate networks (graphs) with deep neural networks. Biomarkers can be placed in proper biological networks as vertices or edges and network learning applications for biomarker discovery are discussed. We finally point out some promising directions for future work about network learning.

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“…There are currently three methods to solve this link prediction problem in our graph, which are path-based methods, embedding methods, and GNNs ( Liu et al, 2022a ; Liu et al, 2022b ; Peng et al, 2022 ). Among these methods, GNNs are a growing family of methods and have shown the most advanced performance.…”

Section: Related Workmentioning

confidence: 99%

DPB-NBFnet: Using neural Bellman-Ford networks to predict DNA-protein binding

Zhuo²,

Lian³

et al. 2022

Front. Pharmacol.

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DNA is a hereditary material that plays an essential role in micro-organisms and almost all other organisms. Meanwhile, proteins are a vital composition and principal undertaker of microbe movement. Therefore, studying the bindings between DNA and proteins is of high significance from the micro-biological point of view. In addition, the binding affinity prediction is beneficial for the study of drug design. However, existing experimental methods to identifying DNA-protein bindings are extremely expensive and time consuming. To solve this problem, many deep learning methods (including graph neural networks) have been developed to predict DNA-protein interactions. Our work possesses the same motivation and we put the latest Neural Bellman-Ford neural networks (NBFnets) into use to build pair representations of DNA and protein to predict the existence of DNA-protein binding (DPB). NBFnet is a graph neural network model that uses the Bellman-Ford algorithms to get pair representations and has been proven to have a state-of-the-art performance when used to solve the link prediction problem. After building the pair representations, we designed a feed-forward neural network structure and got a 2-D vector output as a predicted value of positive or negative samples. We conducted our experiments on 100 datasets from ENCODE datasets. Our experiments indicate that the performance of DPB-NBFnet is competitive when compared with the baseline models. We have also executed parameter tuning with different architectures to explore the structure of our framework.

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RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation

Cited by 47 publications

References 69 publications

Machine learning in the development of targeting microRNAs in human disease

Machine learning in the development of targeting microRNAs in human disease

Network Learning for Biomarker Discovery

DPB-NBFnet: Using neural Bellman-Ford networks to predict DNA-protein binding

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