Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction
暂无分享,去创建一个
Cheng Liang | Huaxiang Zhang | Jiawei Luo | Qiu Xiao | Guanghui Li | Zhenxia Pan | Pingjian Ding | Qiu Xiao | Jiawei Luo | C. Liang | Guanghui Li | Pingjian Ding | Huaxiang Zhang | Zhenxia Pan
[1] Joseph O. Deasy,et al. Identifying Biological Subtypes of Head and Neck Squamous Cell Carcinoma (HNSCC) From Contrast Enhanced CT Scans Using Radiomic and the Cancer Genome Atlas (TCGA) , 2018, International Journal of Radiation Oncology*Biology*Physics.
[2] A. Isaksson,et al. Cross-validation and bootstrapping are unreliable in small sample classification , 2008, Pattern Recognit. Lett..
[3] Yongchun Zuo,et al. iDPF-PseRAAAC: A Web-Server for Identifying the Defensin Peptide Family and Subfamily Using Pseudo Reduced Amino Acid Alphabet Composition , 2015, PloS one.
[4] Vikram Agarwal,et al. Predicting microRNA targeting efficacy in Drosophila , 2017, Genome Biology.
[5] Shuai Liu,et al. Transcriptome Comparisons of Multi-Species Identify Differential Genome Activation of Mammals Embryogenesis , 2019, IEEE Access.
[6] Patrick Ruch,et al. Mapping proteins to disease terminologies: from UniProt to MeSH , 2008, BMC Bioinformatics.
[7] Lei Yang,et al. Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure. , 2015, Molecular bioSystems.
[8] Zhu-Hong You,et al. Novel Human miRNA-Disease Association Inference Based on Random Forest , 2018, Molecular therapy. Nucleic acids.
[9] Liu Qin,et al. The landscape of miRNA-related ceRNA networks for marking different renal cell carcinoma subtypes , 2018, Briefings Bioinform..
[10] Jeong Hun Shin,et al. Bone marrow mesenchymal stem cell-derived vascular endothelial growth factor attenuates cardiac apoptosis via regulation of cardiac miRNA-23a and miRNA-92a in a rat model of myocardial infarction , 2017, PloS one.
[11] Cheng Liang,et al. Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk , 2017, IEEE Access.
[12] Xing Chen,et al. MicroRNAs and complex diseases: from experimental results to computational models , 2019, Briefings Bioinform..
[13] Zenglin Xu,et al. Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification , 2018, IJCAI.
[14] Xing Chen,et al. Long non-coding RNAs and complex diseases: from experimental results to computational models , 2016, Briefings Bioinform..
[15] Heng Tao Shen,et al. Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[16] Xing Chen,et al. EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction , 2018, Cell Death & Disease.
[17] Yongchun Zuo,et al. Function determinants of TET proteins: the arrangements of sequence motifs with specific codes , 2019, Briefings Bioinform..
[18] Dingfang Li,et al. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information , 2017, Molecules.
[19] Cheng Liang,et al. A novel semi-supervised model for miRNA-disease association prediction based on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{d , 2018, Journal of Translational Medicine.
[20] Tao Mei,et al. Graph-based semi-supervised learning with multiple labels , 2009, J. Vis. Commun. Image Represent..
[21] Q. Zou,et al. Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods , 2015, BioMed Research International.
[22] Dong Wang,et al. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases , 2010, Bioinform..
[23] Xiangxiang Zeng,et al. Inferring MicroRNA-Disease Associations by Random Walk on a Heterogeneous Network with Multiple Data Sources , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[24] De-Shuang Huang,et al. iEnhancer‐EL: identifying enhancers and their strength with ensemble learning approach , 2018, Bioinform..
[25] Cheng Liang,et al. KATZMDA: Prediction of miRNA-Disease Associations Based on KATZ Model , 2018, IEEE Access.
[26] Na-Na Guan,et al. Predicting miRNA‐disease association based on inductive matrix completion , 2018, Bioinform..
[27] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[28] Wei Chen,et al. Predicting peroxidase subcellular location by hybridizing different descriptors of Chou' pseudo amino acid patterns. , 2014, Analytical biochemistry.
[29] Wei Chen,et al. A similarity distance of diversity measure for discriminating mesophilic and thermophilic proteins , 2013, Amino Acids.
[30] Xiangxiang Zeng,et al. Prediction of potential disease-associated microRNAs using structural perturbation method , 2017, bioRxiv.
[31] Licheng Jiao,et al. Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation , 2006, Neural Computation.
[32] Shuang Gao,et al. A locality correlation preserving support vector machine , 2014, Pattern Recognit..
[33] Wen Zhang,et al. The linear neighborhood propagation method for predicting long non-coding RNA-protein interactions , 2018, Neurocomputing.
[34] Jia-wei Luo,et al. GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods , 2018, RNA biology.
[35] Na-Na Guan,et al. GIMDA: Graphlet interaction‐based MiRNA‐disease association prediction , 2017, Journal of cellular and molecular medicine.
[36] Fei Li,et al. miR+Pathway: the integration and visualization of miRNA and KEGG pathways , 2020, Briefings Bioinform..
[37] C. Tsatsanis,et al. miRNA and Other Non-Coding RNAs as Promising Diagnostic Markers , 2018, EJIFCC.
[38] Xing Chen,et al. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction , 2017, PLoS Comput. Biol..
[39] Xia Li,et al. Prediction of potential disease-associated microRNAs based on random walk , 2015, Bioinform..
[40] Huanqing Feng,et al. NTSMDA: prediction of miRNA-disease associations by integrating network topological similarity. , 2016, Molecular bioSystems.
[41] Xing Chen,et al. Semi-supervised learning for potential human microRNA-disease associations inference , 2014, Scientific Reports.
[42] Yadong Wang,et al. Prioritization of disease microRNAs through a human phenome-microRNAome network , 2010, BMC Systems Biology.
[43] Maya R. Gupta,et al. Learning kernels from indefinite similarities , 2009, ICML '09.
[44] Cheng Liang,et al. A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations , 2018, Bioinform..
[45] G D Slade,et al. Directed Acyclic Graphs for Oral Disease Research , 2016, Journal of dental research.
[46] Xing Chen,et al. HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction , 2016, Oncotarget.
[47] Xing Chen,et al. RWRMDA: predicting novel human microRNA-disease associations. , 2012, Molecular bioSystems.
[48] Pieter Wesseling,et al. Identification of temozolomide resistance factors in glioblastoma via integrative miRNA/mRNA regulatory network analysis , 2014, Scientific Reports.
[49] David J. Hand,et al. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.
[50] Guangpeng Li,et al. PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition , 2017, Bioinform..
[51] Yang Li,et al. HMDD v2.0: a database for experimentally supported human microRNA and disease associations , 2013, Nucleic Acids Res..
[52] Xia Li,et al. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes , 2013, BMC Systems Biology.
[53] Lei Zhu,et al. Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval , 2017, IEEE Transactions on Cybernetics.