4.7 Article

MDformer: A transformer-based method for predicting miRNA-Disease associations using multi-source feature fusion and maximal meta-path instances encoding

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Biochemical Research Methods

Predicting miRNA-disease associations based on PPMI and attention network

Xuping Xie et al.

Summary: In this study, a computational method called PATMDA is proposed, based on positive point-wise mutual information (PPMI) and attention network, to predict miRNA-disease associations (MDAs). By constructing heterogeneous MDA network and multiple similarity networks of miRNAs and diseases, multi-order proximity features are obtained and fused using a convolutional neural network. An attention network is used to integrate node representations and their heterogeneous neighbor nodes. PATMDA outperforms six state-of-the-art methods in predicting disease-associated miRNAs.

BMC BIOINFORMATICS (2023)

Article Biochemical Research Methods

Improving the identification of miRNA-disease associations with multi-task learning on gene-disease networks

Qiang He et al.

Summary: MicroRNAs (miRNAs) play a vital role in regulating gene expression and predicting miRNA-disease associations using computational methods has become a popular research area. However, existing computational methods often ignore the mediating role of genes and suffer from data sparsity. To address this limitation, we propose a new model called MTLMDA, which leverages both miRNA-disease and gene-disease networks to improve the identification of miRNA-disease associations. Empirical results demonstrate that our model outperforms competitive baselines across various performance metrics.

BRIEFINGS IN BIOINFORMATICS (2023)

Article Biochemical Research Methods

AMHMDA: attention aware multi-view similarity networks and hypergraph learning for miRNA-disease associations identification

Qiao Ning et al.

Summary: In recent years, numerous experiments have shown the significant regulatory roles of microRNAs (miRNAs) in cells, and their abnormal expression is associated with specific diseases. Therefore, investigating the link between miRNAs and diseases is highly valuable for disease prevention and treatment. This study proposes a novel method, AMHMDA, based on Attention aware Multi-view similarity networks and Hypergraph learning for MiRNA-Disease Associations identification.

BRIEFINGS IN BIOINFORMATICS (2023)

Article Biochemical Research Methods

Predicting miRNA-Disease Associations via Node-Level Attention Graph Auto-Encoder

Huizhe Zhang et al.

Summary: This study proposes a novel method called AGAEMD to predict potential miRNA disease associations. It utilizes a node-level attention graph auto-encoder to represent nodes and calculate association scores. Experimental results demonstrate the excellent performance of AGAEMD compared to other methods, and case studies confirm its reliable predictive performance.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2023)

Article Microbiology

Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders

Qingquan Liao et al.

Summary: In order to predict the association between microRNAs and diseases in microbial ecology, a novel model called GCNA-MDA is proposed, which integrates dual-autoencoder and graph convolutional network (GCN). This method uses autoencoders to extract robust representations of microRNAs and diseases, and employs GCN to capture the topological information of microRNA-disease networks. Experimental results show that the proposed method outperforms existing representative methods with a precision of up to 0.8982. These results indicate that the proposed method can serve as a tool for exploring microRNA-disease associations in microbial environments.

FRONTIERS IN MICROBIOLOGY (2023)

Article Biology

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

Biffon Manyura Momanyi et al.

Summary: Understanding the interactions between microRNAs and diseases is crucial for studying the biological mechanisms of human disorders. This study proposes a computational model, CFNCM, for predicting potential miRNA-disease associations by integrating validated similarity information. The model can be used as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders.

COMPUTERS IN BIOLOGY AND MEDICINE (2023)

Article Biochemical Research Methods

Effective drug-target interaction prediction with mutual interaction neural network

Fei Li et al.

Summary: In this article, a new model for drug-target interaction (DTI) prediction, MINN-DTI, is proposed. By combining Interformer with an improved CMPNN, MINN-DTI effectively captures the two-way impact between drugs and targets, resulting in better prediction performance and interpretability.

BIOINFORMATICS (2022)

Article Biochemical Research Methods

Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information

Zhengzheng Lou et al.

Summary: The study proposes a method called MINIMDA, which fuses mixed high-order neighborhood information of miRNAs and diseases in multimodal networks to predict their associations. Experimental results show that MINIMDA outperforms other methods overall and demonstrates excellent performance in case studies for esophageal cancer, colon tumor, and lung cancer, proving its effectiveness.

BRIEFINGS IN BIOINFORMATICS (2022)

Article Biochemical Research Methods

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Jing Hu et al.

Summary: Drug combination therapies are superior to monotherapy in cancer treatment. Computational methods have been developed to predict drug pairs with potential synergistic functions. We propose a deep neural network model called DTSyn based on a multi-head attention mechanism to identify novel drug combinations. DTSyn achieved high performance and improved interpretability by capturing chemical-gene and gene-gene associations and extracting chemical-chemical and chemical-cell line interactions.

BRIEFINGS IN BIOINFORMATICS (2022)

Article Computer Science, Artificial Intelligence

A multi-layer multi-kernel neural network for determining associations between non-coding RNAs and diseases

Chengwei Ai et al.

Summary: Identification of associations between non-coding RNAs and diseases is crucial in the study of pathogenesis. This paper presents a novel deep multiple kernel learning method, MLMKDNN, which effectively predicts the associations between non-coding RNAs and diseases. The proposed method can also be applied to link prediction in other bipartite networks.

NEUROCOMPUTING (2022)

Article Biology

MHDMF: Prediction of miRNA-disease associations based on Deep Matrix Factorization with Multi-source Graph Convolutional Network

Ning Ai et al.

Summary: This study develops a computational framework called MHDMF, which integrates multi-source information and utilizes graph convolutional networks and deep matrix factorization to discover latent disease-miRNA associations. Experimental results show that the proposed framework outperforms other methods in predicting miRNA-disease associations.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Biochemical Research Methods

Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations

Hailin Feng et al.

Summary: This paper presents MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. The model updates the original miRNA-disease association matrix using the most reliable neighbors and reconstructs unknown miRNA-disease associations to improve prediction accuracy. Experimental results show that MRRN outperforms comparable models in terms of accuracy.

BRIEFINGS IN BIOINFORMATICS (2022)

Article Oncology

Cancer Statistics, 2021

Rebecca L. Siegel et al.

Summary: Every year, the American Cancer Society projects the numbers of new cancer cases and deaths in the United States, with the latest data showing a significant decline in lung cancer mortality, while prostate cancer mortality has plateaued and breast and colorectal cancer mortality have slowed. Improvements in treatment have accelerated progress against lung cancer, leading to a record drop in overall cancer mortality.

CA-A CANCER JOURNAL FOR CLINICIANS (2021)

Article Biochemical Research Methods

Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction

Xinru Tang et al.

Summary: The study developed a model called MMGCN to predict potential miRNA-disease associations, achieving superior performance on two datasets and validating the effectiveness of the multichannel attention mechanism and multisource data in association prediction.

BRIEFINGS IN BIOINFORMATICS (2021)

Article Biochemistry & Molecular Biology

HMDD v3.0: a database for experimentally supported human microRNA-disease associations

Zhou Huang et al.

NUCLEIC ACIDS RESEARCH (2019)

Article Biochemical Research Methods

Prediction of potential disease-associated microRNAs using structural perturbation method

Xiangxiang Zeng et al.

BIOINFORMATICS (2018)

Article Medicine, Research & Experimental

Novel Human miRNA-Disease Association Inference Based on Random Forest

Xing Chen et al.

MOLECULAR THERAPY-NUCLEIC ACIDS (2018)

Article Biochemistry & Molecular Biology

RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction

Xing Chen et al.

RNA BIOLOGY (2017)

Article Multidisciplinary Sciences

Prediction of miRNA-disease associations with a vector space model

Claude Pasquier et al.

SCIENTIFIC REPORTS (2016)

Article Multidisciplinary Sciences

Network Consistency Projection for Human miRNA-Disease Associations Inference

Changlong Gu et al.

SCIENTIFIC REPORTS (2016)

Article Multidisciplinary Sciences

MicroRNA Markers for the Diagnosis of Pancreatic and Biliary-Tract Cancers

Motohiro Kojima et al.

PLOS ONE (2015)

Article Biochemistry & Molecular Biology

HMDD v2.0: a database for experimentally supported human microRNA and disease associations

Yang Li et al.

NUCLEIC ACIDS RESEARCH (2014)

Article Biochemical Research Methods

miRCancer: a microRNA-cancer association database constructed by text mining on literature

Boya Xie et al.

BIOINFORMATICS (2013)

Article Biochemical Research Methods

Gaussian interaction profile kernels for predicting drug-target interaction

Twan van Laarhoven et al.

BIOINFORMATICS (2011)

Article Mathematical & Computational Biology

Integrative network analysis reveals active microRNAs and their functions in gastric cancer

Chien-Wei Tseng et al.

BMC SYSTEMS BIOLOGY (2011)

Article Biochemistry & Molecular Biology

miR2Disease: a manually curated database for microRNA deregulation in human disease

Qinghua Jiang et al.

NUCLEIC ACIDS RESEARCH (2009)

Article Biochemical Research Methods

MicroRNA detection by northern blotting using locked nucleic acid probes

Eva Varallyay et al.

NATURE PROTOCOLS (2008)

Review Biotechnology & Applied Microbiology

MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype

Cherie Blenkiron et al.

GENOME BIOLOGY (2007)