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Article
Biochemical Research Methods
Yang Li et al.
Summary: Identifying disease-gene associations is crucial for understanding molecular mechanisms, diagnosing, and treating diseases. Deep learning methods have achieved great success in this field. However, existing research either builds networks based on a single data source or on multi-source data with artificially defined meta-paths. We propose an end-to-end disease-gene association prediction model that integrates heterogeneous information and outperforms state-of-the-art methods.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Tingting Zhao et al.
Summary: Large-scale multiple perturbation experiments can provide a detailed understanding of molecular pathways in response to genetic and environmental changes. A method based on the model-X knockoffs framework and Deep Neural Networks is proposed to identify significant gene expression changes. Identification of genes that respond to specific perturbation stressors can improve the understanding of disease mechanisms and aid in the identification of new drug targets.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Xinru Ruan et al.
Summary: In this study, we propose a novel method using multi-view self-supervised contrastive learning (MSGCL) for miRNA-disease association prediction. By optimizing the graph structure and utilizing the known association network, we enhance the latent representation of association predictions. Experimental results demonstrate that our method outperforms state-of-the-art methods with an improvement of 2.79% in AUC and 3.20% in AUPR.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Weiqi Zhai et al.
Summary: HPO-based approaches are popular for genomic diagnostics of rare diseases, but they do not fully utilize available information on disease and patient phenotypes. We present a new method called Phen2Disease that prioritizes diseases and genes using semantic similarity between phenotype sets. Our experiments show that Phen2Disease outperforms state-of-the-art methods, especially in cohorts with fewer HPO terms. We also find that patients with higher information content scores have more accurate predictions. Phen2Disease provides ranked diseases and patient HPO terms, offering a novel approach for rare disease diagnostics.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
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
Biochemical Research Methods
Huan Zhao et al.
Summary: Numerous experiments have demonstrated the abnormal expression of microRNA (miRNA) in complex human diseases. Identifying the associations between miRNAs and diseases is crucial for clinical medicine, but traditional experimental methods are often inefficient. Therefore, a deep learning method called NSAMDA, based on neighbor selection graph attention networks, is proposed to predict miRNA-disease associations. The NSAMDA model achieves satisfactory performance in predicting miRNA-disease associations, surpassing the most advanced model, as demonstrated through experiments on various diseases.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
S. Sujamol et al.
Summary: This article proposes a two-stage feature pruning approach based on miRNA feature similarity fusion for predicting unknown miRNA-disease associations. The approach utilizes a deep attention autoencoder and recursive feature elimination with cross-validation (RFECV) to prune features and a Random Forest classifier for association prediction. The results show that the proposed approach outperforms recent methodologies for predicting miRNA-disease associations.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Jiwen Liu et al.
Summary: An increasing number of studies have confirmed the significance of microRNAs (miRNAs) in human diseases, and their aberrant expression affects disease onset and progression. The discovery of disease-associated miRNAs as biomarkers has advanced disease pathology and clinical medicine. However, only a small portion of miRNA-disease correlations have undergone biological validation, which is costly and inefficient. Therefore, developing efficient and accurate computational methods to predict miRNA-disease associations is crucial. This paper proposes a novel miRNA-disease association prediction algorithm, GCNPCA, based on Graph Convolutional neural Networks and Principal Component Analysis, which demonstrates high accuracy in cross-validation experiments with AUC and AUPR scores of 0.983 and 0.988 respectively.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Review
Neurosciences
Karolina Anna Kolosowska et al.
Summary: Information processing in neuronal circuits relies on proper development and interplay between principal and inhibitory interneurons. miRNA-dependent gene regulation plays a crucial role in the development and function of inhibitory interneurons, affecting their migration, maturation, survival, and cognitive function. Dysregulation of miRNAs in GABAergic interneurons may contribute to neurodevelopmental and neuropsychiatric disorders.
FRONTIERS IN CELLULAR NEUROSCIENCE
(2023)
Review
Immunology
Laura Zapata-Martinez et al.
Summary: MicroRNAs (miRNAs) play a regulatory role in cardiovascular pathophysiology, particularly in inflammatory cardiovascular diseases. Candidate miRNAs such as miR-21, miR-33, miR-34a, miR-146a, miR-155, and miR-223 functionally regulate thromboinflammation. Modulating the levels of these miRNAs may improve the diagnosis and prognosis of cardiovascular events in inflammatory diseases.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Genetics & Heredity
Zheyu Niu et al.
Summary: miRNAs play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules. In order to predict novel SM-miRNA associations, we propose a miRNA and small molecule association prediction model (GCNNMMA) based on ensemble learning, graph neural networks (GNNs), and convolutional neural networks (CNNs). Experimental results show that GCNNMMA outperforms other comparison models in cross-validation tests on two different datasets.
FRONTIERS IN GENETICS
(2023)
Review
Endocrinology & Metabolism
Julika Huber et al.
Summary: MicroRNAs (miRNAs) play a crucial role in regulating gene expression and have shown potential as biomarkers for diagnosis and monitoring in various diseases. This review focuses on circulating and extracellular vesicle-derived miRNAs as biomarkers in bone-related diseases, with a specific emphasis on osteoporosis and osteosarcoma. The review covers the history and biology of miRNAs, different types of biomarkers, and the current understanding of miRNAs as biomarkers in bone-related diseases. Limitations in miRNA biomarker research and future perspectives are also discussed.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Biochemical Research Methods
Wenxiang Zhang et al.
Summary: Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. A ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Genwei Han et al.
Summary: A high-efficiency algorithm combining biological information and convolutional neural network was proposed to predict the correlation between miRNA and disease. Experimental results showed that the algorithm outperformed other classic classifiers and existing algorithms in predicting the miRNA-disease association.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Oncology
Tao Duan et al.
Summary: miRNA is a potential therapeutic target due to its complex gene regulation mechanism, and its abnormal expression can cause drug resistance, affecting the therapeutic effect of diseases. Developing computational methods to predict miRNA-drug resistance associations is of practical value for designing effective drugs or combinations.
FRONTIERS IN ONCOLOGY
(2022)
Article
Biology
Wang Meixi et al.
Summary: MicroRNAs (miRNAs) play important roles in various biological processes, and computational prediction of their association with diseases is crucial for disease diagnosis and treatment. The MRWMDA model proposed in this paper, utilizing a multiple random walk with restart algorithm, shows significant improvement in predicting miRNA-disease associations and detecting potential disease biomarkers.
Article
Cell Biology
Yifei Yu et al.
Summary: miRNAs play a crucial role in regulating gene expression, while the exchange of miRNAs within exosomes is important in CNS diseases. Exosomal miRNAs can serve as a novel therapeutic strategy and diagnostic tool for neurological diseases.
MOLECULAR AND CELLULAR BIOCHEMISTRY
(2021)
Article
Cell Biology
Lei Li et al.
Summary: MicroRNAs, as non-coding RNAs, are closely related to complex biological processes and human diseases. The study introduced a novel model, SNFIMCMDA, combining similarity network fusion and inductive matrix completion. Global leave-one-out cross-validation and five-fold cross-validation were utilized to validate the model efficacy, with case studies on three human diseases supporting its effectiveness.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Computer Science, Hardware & Architecture
Ling-Yun Dai et al.
Summary: This paper introduces a new prediction method LWBRW based on known MDAs, which uses miRNA, disease, and known MDA networks to predict potential MDAs. By employing logistic function and weighted K-nearest neighbors method to improve accuracy, and inferring potential MDAs by bi-random walk, the predictive ability of the method is confirmed through cross-validation and leave-one-out cross-validation. Case studies demonstrate the outstanding ability of the LWBRW method to explore potential MDAs.
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
(2021)
Article
Multidisciplinary Sciences
Ang Li et al.
Summary: Many studies have shown that variations and disorders in miRNAs are important factors in diseases, making the identification of disease-related miRNAs a hot topic in biological research. Computational prediction models are necessary for predicting disease-related miRNAs accurately and effectively.
Article
Genetics & Heredity
Jia Qu et al.
Summary: The study developed a computing model for predicting miRNA-disease associations based on heterogeneous networks, with good performance demonstrated. Case studies showed that the model also performed well in prioritizing candidate miRNAs.
FRONTIERS IN GENETICS
(2021)
Article
Urology & Nephrology
Jia Di et al.
Summary: MiR-21 delivered by microvesicles from renal tubular epithelial cells can induce cardiomyocyte hypertrophy, providing new insights into the mechanism and treatment of CKD-related cardiac dysfunction.
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Hsi-Yuan Huang et al.
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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2019)
Article
Cell Biology
Zhufeng Lu et al.
CELL DEATH & DISEASE
(2019)
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Biochemistry & Molecular Biology
Zhou Huang et al.
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Review
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VASCULAR PHARMACOLOGY
(2019)
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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
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