Applications of Machine Learning in miRNA Discovery and Target Prediction
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Abhishek Kumar | Alisha Parveen | Syed H. Mustafa | Pankaj Yadav | Alisha Parveen | Abhishek Kumar | Pankaj Yadav
[1] Zhongming Zhao,et al. MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets , 2015, Scientific Reports.
[2] Harsh Dweep,et al. Obtaining miRNA‐Target Interaction Information from miRWalk2.0 , 2016, Current protocols in bioinformatics.
[3] R. Shamir,et al. Towards computational prediction of microRNA function and activity , 2010, Nucleic acids research.
[4] Byoung-Tak Zhang,et al. ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs , 2006, Nucleic Acids Res..
[5] Brendan J. Frey,et al. Bayesian Inference of MicroRNA Targets from Sequence and Expression Data , 2007, J. Comput. Biol..
[6] Jun Ding,et al. TarPmiR: a new approach for microRNA target site prediction , 2016, Bioinform..
[7] V. Ambros,et al. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 , 1993, Cell.
[8] MicroRNA methodology: advances in miRNA technologies. , 2014, Methods in molecular biology.
[9] Darby Tien-Hao Chang,et al. Using a kernel density estimation based classifier to predict species-specific microRNA precursors , 2008, BMC Bioinformatics.
[10] Samuel E. Aggrey,et al. Specificity and Sensitivity of PROMIR, ERPIN and MIR-ABELA in Predicting Pre-MicroRNAs in the Chicken Genome , 2008, Silico Biol..
[11] Brad T. Sherman,et al. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.
[12] Athanasios K. Tsakalidis,et al. Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role , 2013, J. Biomed. Informatics.
[13] Feilong Cao,et al. Distributed support vector machine in master-slave mode , 2018, Neural Networks.
[14] Fei Li,et al. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine , 2005, BMC Bioinformatics.
[15] Sheau-Ling Hsieh,et al. Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians , 2013, Journal of medical Internet research.
[16] V. Kim,et al. Regulation of microRNA biogenesis , 2014, Nature Reviews Molecular Cell Biology.
[17] G. Obernosterer,et al. Post-transcriptional regulation of microRNA expression. , 2006, RNA.
[18] V. Chinnusamy,et al. Genome-wide identification and analysis of biotic and abiotic stress regulation of small heat shock protein (HSP20) family genes in bread wheat. , 2017, Journal of plant physiology.
[19] Xiaowei Wang,et al. miRDB: an online resource for microRNA target prediction and functional annotations , 2014, Nucleic Acids Res..
[20] C. Deltas,et al. microRNAs: a newly described class of encoded molecules that play a role in health and disease. , 2010, Hippokratia.
[21] Gabriele Sales,et al. MAGIA, a web-based tool for miRNA and Genes Integrated Analysis , 2010, Nucleic Acids Res..
[22] Dennis Medved,et al. Predicting the outcome for patients in a heart transplantation queue using deep learning , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[23] Mou-Ze Liu,et al. Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients , 2017, Scientific Reports.
[24] Peter F. Stadler,et al. ViennaRNA Package 2.0 , 2011, Algorithms for Molecular Biology.
[25] Jan Gorodkin,et al. Principles and limitations of computational microRNA gene and target finding. , 2007, DNA and cell biology.
[26] M. Bhaskaran,et al. MicroRNAs: history, biogenesis, and their evolving role in animal development and disease. , 2014, Veterinary pathology.
[27] M. Yousef,et al. Computational miRNomics , 2016, Journal of integrative bioinformatics.
[28] Fabrice Jotterand,et al. Artificial intelligence, physiological genomics, and precision medicine. , 2018, Physiological genomics.
[29] G. Ruvkun,et al. Dual Regulation of the lin-14 Target mRNA by the lin-4 miRNA , 2013, PloS one.
[30] Panayiotis V. Benos,et al. HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models , 2009, BMC Bioinformatics.
[31] R. Darnell,et al. Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data , 2018, Arthritis & rheumatology.
[32] Taishin Kin,et al. miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity. , 2007, RNA.
[33] G. Michlewski. Posttranscriptional regulation of microRNA expression , 2015 .
[34] Louise C. Showe,et al. Naïve Bayes for microRNA target predictions - machine learning for microRNA targets , 2007, Bioinform..
[35] Louise C. Showe,et al. Bioinformatics Original Paper Combining Multi-species Genomic Data for Microrna Identification Using a Naı¨ve Bayes Classifier , 2022 .
[36] Ana Kozomara,et al. miRBase: annotating high confidence microRNAs using deep sequencing data , 2013, Nucleic Acids Res..