De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures.
about
Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potentialMaturePred: efficient identification of microRNAs within novel plant pre-miRNAsA Review of Computational Methods for Finding Non-Coding RNA GenesMicroRNA prediction using a fixed-order Markov model based on the secondary structure patternmiRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVMMicroRNA identification using linear dimensionality reduction with explicit feature mapping.MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scansMammalian microRNA prediction through a support vector machine model of sequence and structure.Ab initio identification of human microRNAs based on structure motifs.GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine techniqueSelf containment, a property of modular RNA structures, distinguishes microRNAsUsing a kernel density estimation based classifier to predict species-specific microRNA precursors.In silico miRNA prediction in metazoan genomes: balancing between sensitivity and specificity.Identification and analysis of miRNAs in human breast cancer and teratoma samples using deep sequencingOntology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs.Identification and characterization of novel amphioxus microRNAs by Solexa sequencingPrediction of novel precursor miRNAs using a context-sensitive hidden Markov model (CSHMM).Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithmApplication of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes.The discriminant power of RNA features for pre-miRNA recognitionEnsemble-based classification approach for micro-RNA mining applied on diverse metagenomic sequencesAnalysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features.Identification of microRNA precursors based on random forest with network-level representation method of stem-loop structure.RAG: an update to the RNA-As-Graphs resourceIntegrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression.Identification and differential expression of microRNAs during metamorphosis of the Japanese flounder (Paralichthys olivaceus).Genome-wide identification of Medicago truncatula microRNAs and their targets reveals their differential regulation by heavy metal.Sequence-based classification using discriminatory motif feature selection.Integrated sequence-structure motifs suffice to identify microRNA precursors.Identification and characterization of cold-responsive microRNAs in tea plant (Camellia sinensis) and their targets using high-throughput sequencing and degradome analysismiR-BAG: bagging based identification of microRNA precursors.Computational and experimental identification of mirtrons in Drosophila melanogaster and Caenorhabditis elegans.HuntMi: an efficient and taxon-specific approach in pre-miRNA identification.Serum peptidomic profiling identifies a minimal residual disease detection and prognostic biomarker for patients with acute leukemia.miRBoost: boosting support vector machines for microRNA precursor classification.Computational prediction of microRNAs from Toxoplasma gondii potentially regulating the hosts' gene expressionViralmiR: a support-vector-machine-based method for predicting viral microRNA precursorsLBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length.MicroRNA categorization using sequence motifs and k-mers
P2860
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P2860
De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures.
description
2007 nî lūn-bûn
@nan
2007年の論文
@ja
2007年学术文章
@wuu
2007年学术文章
@zh
2007年学术文章
@zh-cn
2007年学术文章
@zh-hans
2007年学术文章
@zh-my
2007年学术文章
@zh-sg
2007年學術文章
@yue
2007年學術文章
@zh-hant
name
De novo SVM classification of ...... nd intrinsic folding measures.
@en
De novo SVM classification of ...... nd intrinsic folding measures.
@nl
type
label
De novo SVM classification of ...... nd intrinsic folding measures.
@en
De novo SVM classification of ...... nd intrinsic folding measures.
@nl
prefLabel
De novo SVM classification of ...... nd intrinsic folding measures.
@en
De novo SVM classification of ...... nd intrinsic folding measures.
@nl
P2860
P356
P1433
P1476
De novo SVM classification of ...... nd intrinsic folding measures.
@en
P2093
Kwang Loong Stanley Ng
Santosh K Mishra
P2860
P304
P356
10.1093/BIOINFORMATICS/BTM026
P407
P577
2007-01-31T00:00:00Z