A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome
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[1] Jiu-Xin Tan,et al. Evaluation of different computational methods on 5-methylcytosine sites identification , 2020, Briefings Bioinform..
[2] Feng Zhu,et al. VARIDT 1.0: variability of drug transporter database , 2019, Nucleic Acids Res..
[3] Miriam A. M. Capretz,et al. Machine Learning With Big Data: Challenges and Approaches , 2017, IEEE Access.
[4] Bo Li,et al. NOREVA: normalization and evaluation of MS-based metabolomics data , 2017, Nucleic Acids Res..
[5] Xia Sun,et al. Drug and Nondrug Classification Based on Deep Learning with Various Feature Selection Strategies , 2018 .
[6] Jijun Tang,et al. Prediction of human protein subcellular localization using deep learning , 2017, J. Parallel Distributed Comput..
[7] Xiangxiang Zeng,et al. Prediction of potential disease-associated microRNAs using structural perturbation method , 2017, bioRxiv.
[8] T. P. Centeno,et al. DNA methylation changes in plasticity genes accompany the formation and maintenance of memory , 2015, Nature Neuroscience.
[9] Leyi Wei,et al. A novel hierarchical selective ensemble classifier with bioinformatics application , 2017, Artif. Intell. Medicine.
[10] Wanying Xu,et al. OAHG: an integrated resource for annotating human genes with multi-level ontologies , 2016, Scientific Reports.
[11] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[12] Bifeng Yuan,et al. N 6-Hydroxymethyladenine: a hydroxylation derivative of N6-methyladenine in genomic DNA of mammals , 2018, Nucleic acids research.
[13] Wei Tao,et al. A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae. , 2019, Briefings in functional genomics.
[14] Fei Guo,et al. Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier , 2017, Artif. Intell. Medicine.
[15] Adam Ameur,et al. Single-Molecule Sequencing: Towards Clinical Applications. , 2019, Trends in biotechnology.
[16] Feng Zhu,et al. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics , 2019, Nucleic Acids Res..
[17] Jijun Tang,et al. Predicting protein-protein interactions via multivariate mutual information of protein sequences , 2016, BMC Bioinformatics.
[18] Yuzong Chen,et al. What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors' Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation. , 2018, ACS chemical neuroscience.
[19] Jin Zhao,et al. Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome , 2017, Artif. Intell. Medicine.
[20] N. Le. iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule , 2019, Molecular Genetics and Genomics.
[21] Binhua Tang,et al. Recent Advances of Deep Learning in Bioinformatics and Computational Biology , 2019, Front. Genet..
[22] Fei Li,et al. MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model , 2019, Bioinform..
[23] Xiangxiang Zeng,et al. MOEA/HD: A Multiobjective Evolutionary Algorithm Based on Hierarchical Decomposition , 2019, IEEE Transactions on Cybernetics.
[24] Tyson A. Clark,et al. Direct detection of DNA methylation during single-molecule, real-time sequencing , 2010, Nature Methods.
[25] Xiangrong Liu,et al. On solutions and representations of spiking neural P systems with rules on synapses , 2019, Inf. Sci..
[26] Jijun Tang,et al. Identification of Protein–Protein Interactions via a Novel Matrix-Based Sequence Representation Model with Amino Acid Contact Information , 2016, International journal of molecular sciences.
[27] Lei Deng,et al. Prediction of Protein S-Sulfenylation Sites Using a Deep Belief Network , 2018, Current Bioinformatics.
[28] B. Liu,et al. An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier , 2013, BioMed research international.
[29] Shunmin He,et al. N6-Methyladenine DNA Modification in Drosophila , 2015, Cell.
[30] Jiu-Xin Tan,et al. Identification of hormone binding proteins based on machine learning methods. , 2019, Mathematical biosciences and engineering : MBE.
[31] Jionglong Su,et al. WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach , 2019, Nucleic acids research.
[32] Gaotao Shi,et al. CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency. , 2017, Journal of proteome research.
[33] Jijun Tang,et al. Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC. , 2019, Journal of theoretical biology.
[34] S Joshua Swamidass,et al. A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data , 2018, Nature Genetics.
[35] Liang Kong,et al. i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features , 2019, Genes.
[36] Feng Zhu,et al. Differentiating Physicochemical Properties between Addictive and Nonaddictive ADHD Drugs Revealed by Molecular Dynamics Simulation Studies. , 2017, ACS chemical neuroscience.
[37] Xiaofeng Li,et al. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies , 2019, Briefings Bioinform..
[38] Guangmin Liang,et al. An Efficient Classifier for Alzheimer’s Disease Genes Identification , 2018, Molecules.
[39] Lixia Yao,et al. Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains. , 2019, Molecular & cellular proteomics : MCP.
[40] Liang Cheng,et al. gutMDisorder: a comprehensive database for dysbiosis of the gut microbiota in disorders and interventions , 2019, Nucleic acids research.
[41] Zhibin Lv,et al. Protein Function Prediction: From Traditional Classifier to Deep Learning , 2019, Proteomics.
[42] Lin Gao,et al. Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity , 2018, International journal of biological sciences.
[43] Jing Zhang,et al. Prediction of Novel Drugs for Hepatocellular Carcinoma Based on Multi-Source Random Walk , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[44] Hao Lin,et al. iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice , 2019, Front. Genet..
[45] P. Modrich,et al. Initiation of methyl-directed mismatch repair. , 1992, The Journal of biological chemistry.
[46] Xiangrong Liu,et al. deepDR: a network-based deep learning approach to in silico drug repositioning , 2019, Bioinform..
[47] Dong-Qing Wei,et al. PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method , 2018, Front. Microbiol..
[48] Liang Yu,et al. Conserved Disease Modules Extracted From Multilayer Heterogeneous Disease and Gene Networks for Understanding Disease Mechanisms and Predicting Disease Treatments , 2019, Front. Genet..
[49] M. Huss,et al. A primer on deep learning in genomics , 2018, Nature Genetics.
[50] Ming Zhang,et al. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble. , 2018, Analytical biochemistry.
[51] Fei Guo,et al. MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association , 2018, Front. Genet..
[52] Guohua Huang,et al. The Advances and Challenges of Deep Learning Application in Biological Big Data Processing , 2017, Current Bioinformatics.
[53] Jie Sun,et al. DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function , 2018, Bioinform..
[54] Xiangxiang Zeng,et al. An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization , 2019, IEEE Transactions on Cybernetics.
[55] Yasen Jiao,et al. Performance measures in evaluating machine learning based bioinformatics predictors for classifications , 2016, Quantitative Biology.
[56] Jijun Tang,et al. Identification of drug-target interactions via multiple information integration , 2017, Inf. Sci..
[57] A. Giangrande,et al. Drosophila melanogaster as a Model to Study the Multiple Phenotypes, Related to Genome Stability of the Fragile-X Syndrome , 2019, Front. Genet..
[58] Kil To Chong,et al. iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule , 2019, Chemometrics and Intelligent Laboratory Systems.
[59] Yurong Liu,et al. A survey of deep neural network architectures and their applications , 2017, Neurocomputing.
[60] Han Zhang,et al. BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches , 2019, Nucleic acids research.
[61] Jijun Tang,et al. FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association , 2018, BMC Genomics.
[62] Byunghan Lee,et al. Deep learning in bioinformatics , 2016, Briefings Bioinform..
[63] Minghui He,et al. N6-Methyladenine DNA Modification in the Human Genome. , 2018, Molecular cell.
[64] Pengfei Wang,et al. Robust feature learning for online discriminative tracking without large-scale pre-training , 2017, Frontiers of Computer Science.
[65] Xiangxiang Zeng,et al. Prediction and Validation of Disease Genes Using HeteSim Scores , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[66] B. Henrissat,et al. Multi-omic analyses of exogenous nutrient bag decomposition by the black morel Morchella importuna reveal sustained carbon acquisition and transferring. , 2019, Environmental microbiology.
[67] Guangmin Liang,et al. SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins , 2018, International journal of molecular sciences.
[68] Jiajie Peng,et al. InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk , 2018, BMC Genomics.
[69] 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.
[70] Fabian J Theis,et al. Deep learning: new computational modelling techniques for genomics , 2019, Nature Reviews Genetics.
[71] Guangmin Liang,et al. A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides , 2018, Genes.
[72] Q. Zou,et al. Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA , 2018, RNA.
[73] Ruimao Zhang,et al. Learning deep representations for semantic image parsing: a comprehensive overview , 2018, Frontiers of Computer Science.
[74] Q. Zou,et al. Deep learning in omics: a survey and guideline , 2018, Briefings in functional genomics.
[75] Dong-Qing Wei,et al. Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-Based Label Space Division Method , 2019, J. Chem. Inf. Model..
[76] Michael C. Schatz,et al. Accurate detection of complex structural variations using single molecule sequencing , 2017, Nature Methods.
[77] Alfonso Rodríguez-Patón,et al. Meta-Path Methods for Prioritizing Candidate Disease miRNAs , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[78] Yan Lin,et al. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators , 2018, Bioinform..
[79] R. Morgan,et al. N4-cytosine DNA methylation regulates transcription and pathogenesis in Helicobacter pylori , 2018, Nucleic acids research.
[80] Jijun Tang,et al. Identification of drug-side effect association via multiple information integration with centered kernel alignment , 2019, Neurocomputing.
[81] Guangmin Liang,et al. k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification , 2019, Front. Genet..
[82] Gwang Lee,et al. SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome , 2019, Molecular therapy. Nucleic acids.