Computational biology and chemistry Special section editorial: Computational analyses for miRNA

[1]  Wei Wang,et al.  LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions , 2020, Comput. Biol. Chem..

[2]  Hao Zhang,et al.  Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model , 2020, Comput. Biol. Chem..

[3]  Limin Jiang,et al.  Identification of human microRNA-disease association via hypergraph embedded bipartite local model , 2020, Comput. Biol. Chem..

[4]  Hang Wei,et al.  iPiDA-sHN: Identification of Piwi-interacting RNA-disease associations by selecting high quality negative samples , 2020, Comput. Biol. Chem..

[5]  Dariusz Mrozek,et al.  A review of Cloud computing technologies for comprehensive microRNA analyses , 2020, Comput. Biol. Chem..

[6]  Kil To Chong,et al.  ncRDeep: Non-coding RNA classification with convolutional neural network , 2020, Comput. Biol. Chem..

[7]  Claude Pasquier,et al.  Computational prediction of miRNA/mRNA duplexomes at the whole human genome scale reveals functional subnetworks of interacting genes with embedded miRNA annealing motifs , 2020, Comput. Biol. Chem..

[8]  Maozu Guo,et al.  Data fusion-based algorithm for predicting miRNA-Disease associations , 2020, Comput. Biol. Chem..

[9]  Müşerref Duygu Saçar Demirci,et al.  Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection , 2020, bioRxiv.

[10]  Christina Backes,et al.  What's the target: understanding two decades of in silico microRNA-target prediction , 2019, Briefings Bioinform..

[11]  Yang Yang,et al.  Trends in the development of miRNA bioinformatics tools , 2019, Briefings Bioinform..

[12]  Xing Chen,et al.  MicroRNAs and complex diseases: from experimental results to computational models , 2019, Briefings Bioinform..

[13]  Ana Kozomara,et al.  miRBase: from microRNA sequences to function , 2018, Nucleic Acids Res..

[14]  Yuan Zhou,et al.  HMDD v3.0: a database for experimentally supported human microRNA–disease associations , 2018, Nucleic Acids Res..

[15]  Tony Sawford,et al.  Expanding the horizons of microRNA bioinformatics , 2018, RNA.

[16]  Cheng Liang,et al.  A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations , 2018, Bioinform..

[17]  Xiangxiang Zeng,et al.  Prediction of potential disease-associated microRNAs using structural perturbation method , 2017, bioRxiv.

[18]  Silvia Bottini,et al.  Recent computational developments on CLIP-seq data analysis and microRNA targeting implications , 2017, Briefings Bioinform..

[19]  Ralf Hofestädt,et al.  Computational miRNomics – Integrative Approaches , 2017, J. Integr. Bioinform..

[20]  H. Seitz,et al.  microRNA target prediction programs predict many false positives , 2017, Genome research.

[21]  Bogdan Tanasa,et al.  From benchmarking HITS-CLIP peak detection programs to a new method for identification of miRNA-binding sites from Ago2-CLIP data , 2017, Nucleic acids research.

[22]  Gaurav Sablok,et al.  miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA–target interaction data , 2016, Nucleic acids research.

[23]  Yang Liu,et al.  MiRTDL: A Deep Learning Approach for miRNA Target Prediction , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[24]  Fariza Tahi,et al.  miRNAFold: a web server for fast miRNA precursor prediction in genomes , 2016, Nucleic Acids Res..

[25]  Xiaowei Wang,et al.  Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies , 2016, Bioinform..

[26]  Xiangxiang Zeng,et al.  Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks , 2016, Briefings Bioinform..

[27]  Shivakumar Keerthikumar,et al.  ExoCarta: A Web-Based Compendium of Exosomal Cargo. , 2016, Journal of molecular biology.

[28]  Hsien-Da Huang,et al.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database , 2015, Nucleic Acids Res..

[29]  D. Bartel,et al.  Predicting effective microRNA target sites in mammalian mRNAs , 2015, eLife.

[30]  Q. Zou,et al.  Similarity computation strategies in the microRNA-disease network: a survey. , 2015, Briefings in functional genomics.

[31]  Artemis G Hatzigeorgiou,et al.  microTSS: accurate microRNA transcription start site identification reveals a significant number of divergent pri-miRNAs , 2014, Nature Communications.

[32]  M. Zavolan,et al.  Identification and consequences of miRNA–target interactions — beyond repression of gene expression , 2014, Nature Reviews Genetics.

[33]  Jeffrey A. Thompson,et al.  Common features of microRNA target prediction tools , 2014, Front. Genet..

[34]  Hui Zhou,et al.  starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data , 2013, Nucleic Acids Res..

[35]  Yang Li,et al.  HMDD v2.0: a database for experimentally supported human microRNA and disease associations , 2013, Nucleic Acids Res..

[36]  L. Hood,et al.  A Review of Computational Tools in microRNA Discovery , 2013, Front. Genet..

[37]  Hongjing Han,et al.  miRCancer: a microRNA-cancer association database constructed by text mining on literature , 2013, Bioinform..

[38]  Ao Li,et al.  PASmiR: a literature-curated database for miRNA molecular regulation in plant response to abiotic stress , 2013, BMC Plant Biology.

[39]  C. Croce,et al.  MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review , 2012, EMBO molecular medicine.

[40]  Sebastian D. Mackowiak,et al.  miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades , 2011, Nucleic acids research.

[41]  Yufei Huang,et al.  Improving performance of mammalian microRNA target prediction , 2010, BMC Bioinformatics.

[42]  Juan Wang,et al.  Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases , 2010, Bioinform..

[43]  Yufei Huang,et al.  Survey of Computational Algorithms for MicroRNA Target Prediction , 2009, Current genomics.

[44]  Yadong Wang,et al.  miR2Disease: a manually curated database for microRNA deregulation in human disease , 2008, Nucleic Acids Res..

[45]  Q. Cui,et al.  An Analysis of Human MicroRNA and Disease Associations , 2008, PloS one.

[46]  X. Chen,et al.  Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases , 2008, Cell Research.

[47]  Daniel B. Martin,et al.  Circulating microRNAs as stable blood-based markers for cancer detection , 2008, Proceedings of the National Academy of Sciences.

[48]  Jan Krüger,et al.  RNAhybrid: microRNA target prediction easy, fast and flexible , 2006, Nucleic Acids Res..

[49]  D. Bartel,et al.  Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. , 2004, Molecular cell.

[50]  Byunghan Lee,et al.  Advance Access Publication Date: Day Month Year Manuscript Category Deeptarget: End-to-end Learning Framework for Microrna Target Prediction Using Deep Recurrent Neural Networks , 2022 .