Article
Biochemistry & Molecular Biology
Zihao Li, Xing Huang, Yakun Shi, Xiaoyong Zou, Zhanchao Li, Zong Dai
Summary: In this study, a computational method was developed to identify associations between microRNAs and diseases. By constructing a multi-module heterogeneous network and learning features with GAT, the method achieved outstanding prediction performance using SVM.
Article
Genetics & Heredity
Xiaoyu Yang, Linai Kuang, Zhiping Chen, Lei Wang
Summary: The study introduces a novel method called MSBMFHMDA for predicting potential microbe-disease associations, demonstrating reliable performance in cross-validation. The experimental results further confirm the effectiveness of the model in predicting potential associations.
FRONTIERS IN GENETICS
(2021)
Article
Genetics & Heredity
Cunmei Ji, Yutian Wang, Jiancheng Ni, Chunhou Zheng, Yansen Su
Summary: Recent evidence suggests that miRNAs play a critical role in human diseases, but the underlying mechanisms remain unclear. The proposed HGATMDA method shows promising results in predicting miRNA-disease associations, with validation datasets confirming its effectiveness.
FRONTIERS IN GENETICS
(2021)
Article
Biochemistry & Molecular Biology
Yi Yang, Junliang Shang, Yan Sun, Feng Li, Yuanyuan Zhang, Xiang-Zhen Kong, Shengjun Li, Jin-Xing Liu
Summary: A novel method named TLNPMD was developed to predict miRNA-disease associations by introducing drug heuristic information and a bipartite network reconstruction strategy. Comparative experiments and case studies showed that TLNPMD may serve as a promising alternative for predicting miRNA-disease associations.
Article
Genetics & Heredity
Yidong Rao, Minzhu Xie, Hao Wang
Summary: This paper proposes a matrix completion model with bounded nuclear norm regularization, called BNNRMDA, to predict potential miRNA-disease associations. BNNRMDA makes full use of available information of miRNAs and diseases and can handle noisy data. Experimental results show that BNNRMDA achieves the best performance compared to four state-of-the-art methods.
FRONTIERS IN GENETICS
(2022)
Article
Biochemical Research Methods
Qiguo Dai, Zhaowei Wang, Ziqiang Liu, Xiaodong Duan, Jinmiao Song, Maozu Guo
Summary: In this study, a new computational method called ERMDA is proposed to predict potential disease-related miRNAs. The method addresses the challenge of sample imbalance by using a resampling strategy to build balanced training subsets. It extracts miRNA and disease feature representations and applies a feature selection approach to reduce redundancy. Experimental results demonstrate that ERMDA outperforms other methods on testing sets, and case studies confirm its prediction capability for identifying disease-related miRNAs.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Buwen Cao, Shuguang Deng, Hua Qin, Jiawei Luo, Guanghui Li, Cheng Liang
Summary: This study successfully inferred miRNA-disease relationships by constructing a miRNA functional similarity network and utilizing an improved K-means algorithm. Experimental results demonstrated that the performance of IK-means algorithm was superior to classical K-means algorithms in identifying new miRNA-disease associations.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2021)
Article
Biochemical Research Methods
Cheng Yan, Guihua Duan, Fang-Xiang Wu, Yi Pan, Jianxin Wang
Summary: With the advancement of high-throughput sequencing technology and microbiology, studies have shown that microbes are associated with human diseases, making it an important topic to identify their associations. This study introduces a low-rank matrix completion method to predict microbe-disease associations by integrating similarities of microbes and diseases.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Wengang Wang, Hailin Chen
Summary: Increasing studies have shown that miRNAs play a critical role in human complex diseases. Predicting miRNA-disease associations is important for disease prevention, diagnosis, and treatment. This study proposes a deep learning-based method, MAGCN, which predicts potential MDAs without using similarity measurements. The results demonstrate the effectiveness of our method in detecting new disease-related miRNAs.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Wengang Wang, Hailin Chen
Summary: Increasing studies have shown that miRNAs play a critical role in the development of complex human diseases. In this study, a deep learning-based method named MAGCN is proposed to predict potential miRNA-disease associations without using any similarity measurements. The method shows improved prediction accuracy compared to state-of-the-art methods.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Sai Zhang, Jin Li, Wei Zhou, Tong Li, Yang Zhang, Jingru Wang
Summary: MiRNA-disease association prediction is important for identifying human disease-related miRNAs and understanding disease pathogenesis. This paper proposes a novel approach called HOP_MDA, which combines both explicit and implicit higher-order proximity information between miRNA and disease to predict their association. The approach uses supervised learning and optimizing weight parameters to create an effective prediction matrix. The results show high AUC values on different datasets and the ability to predict potential miRNAs related to new diseases.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biotechnology & Applied Microbiology
Junliang Shang, Yi Yang, Feng Li, Boxin Guan, Jin-Xing Liu, Yan Sun
Summary: This study proposes a method called BLNIMDA based on a weighted bi-level network for predicting hidden associations between miRNAs and diseases. The method defines different types of miRNA-disease associations and introduces affinity weights evaluation from bidirectional information distribution strategy and defined association types, ensuring comprehensive and accurate prediction of miRNA-disease associations. The results show that BLNIMDA outperforms other computational methods in terms of predictive performance.
Article
Mathematical & Computational Biology
Yi Shen, Jin-Xing Liu, Meng-Meng Yin, Chun-Hou Zheng, Ying-Lian Gao
Summary: This study proposed a space projection model based on block matrix for predicting miRNA-disease associations (BMPMDA). By utilizing matrix completion and linear neighborhood similarity, the model achieved high accuracy in prediction and identified numerous novel associations in existing databases.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2023)
Article
Biology
Benzhi Dong, Weidong Sun, Dali Xu, Guohua Wang, Tianjiao Zhang
Summary: There is increasing evidence that microRNAs play a crucial role in the diagnosis, treatment, and prognostic assessment of diseases. However, verifying the association between microRNAs and diseases through traditional experimental methods is often inefficient. In order to improve predictive performance, researchers have proposed a transformer-based prediction model that utilizes multiple features of microRNAs and diseases, resulting in higher accuracy and effectiveness compared to existing methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Biochemical Research Methods
Tangbo Zhong, Zhengwei Li, Zhu-Hong You, Ru Nie, Huan Zhao
Summary: Abnormal expression of miRNAs is often associated with specific diseases, and the GRPAMDA model, combining graph random propagation network and attention network, shows high accuracy and reliability in predicting miRNA-disease associations.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Ying Liang, Kunlong Qiu, Bo Liao, Wen Zhu, Xuanlin Huang, Lin Li, Xiangtao Chen, Keqin Li
Article
Biochemical Research Methods
Jiawei Luo, Pingjian Ding, Cheng Liang, Buwen Cao, Xiangtao Chen
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2017)
Article
Computer Science, Information Systems
Pingjian Ding, Jiawei Luo, Cheng Liang, Jie Cai, Ying Liu, Xiangtao Chen
Article
Computer Science, Artificial Intelligence
Jiawei Luo, Pingjian Ding, Cheng Liang, Xiangtao Chen
Article
Computer Science, Artificial Intelligence
Qisheng Zhang, Wen Zhu, Bo Liao, Xiangtao Chen, Lijun Cai
SWARM AND EVOLUTIONARY COMPUTATION
(2018)
Article
Metallurgy & Metallurgical Engineering
Zhang Hong-liang, Zou Zhong, Li Jie, Chen Xiang-tao
JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY
(2008)
Article
Multidisciplinary Sciences
Wei Liu, Wen Zhu, Bo Liao, Xiangtao Chen
Article
Physics, Multidisciplinary
Xiangtao Chen, Juan Li
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
Ronghui Wu, Qing Li, Xiangtao Chen
APPLIED INTELLIGENCE
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Xiangtao Chen, Yajing Gao, Siqi Ren
ISCSIC'18: PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND INTELLIGENT CONTROL
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Xiangtao Chen, Ziping Guan
SECOND INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION
(2017)
Proceedings Paper
Computer Science, Information Systems
Xiangtao Chen, Juan Li
PROCEEDINGS OF THE 2018 2ND INTERNATIONAL CONFERENCE ON COMPUTE AND DATA ANALYSIS (ICCDA 2018)
(2015)