Identification of real microRNA precursors with a pseudo structure status composition approach
about
Overlapping Community Detection based on Network Decomposition.Identifying anticancer peptides by using improved hybrid compositionsSurvey of Natural Language Processing Techniques in Bioinformatics.Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarityPse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.Accurate prediction of nuclear receptors with conjoint triad featureCrossNorm: a novel normalization strategy for microarray data in cancersBenchmark data for identifying N(6)-methyladenosine sites in the Saccharomyces cerevisiae genomeDNA binding protein identification by combining pseudo amino acid composition and profile-based protein representationA functional module-based exploration between inflammation and cancer in esophagus.DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.Construction of the High-Density Genetic Linkage Map and Chromosome Map of Large Yellow Croaker (Larimichthys crocea).Predicting cancerlectins by the optimal g-gap dipeptides.JNSViewer-A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures.Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions.DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.Recombination spot identification Based on gapped k-mers.Combined sequence and sequence-structure based methods for analyzing FGF23, CYP24A1 and VDR genes.Protein Remote Homology Detection Based on an Ensemble Learning Approach.iACP: a sequence-based tool for identifying anticancer peptides.An efficient method for protein function annotation based on multilayer protein networksRelapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinomaComprehensive analysis of lncRNA expression profiles reveals a novel lncRNA signature to discriminate nonequivalent outcomes in patients with ovarian cancer.iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.An improved method for identification of small non-coding RNAs in bacteria using support vector machine.Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.Similarity computation strategies in the microRNA-disease network: a survey.Recent Advances in Conotoxin Classification by Using Machine Learning Methods.Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species.iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition.pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.
P2860
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P2860
Identification of real microRNA precursors with a pseudo structure status composition approach
description
2015 nî lūn-bûn
@nan
2015 թուականին հրատարակուած գիտական յօդուած
@hyw
2015 թվականին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
Identification of real microRN ...... re status composition approach
@ast
Identification of real microRN ...... re status composition approach
@en
Identification of real microRN ...... re status composition approach
@nl
type
label
Identification of real microRN ...... re status composition approach
@ast
Identification of real microRN ...... re status composition approach
@en
Identification of real microRN ...... re status composition approach
@nl
prefLabel
Identification of real microRN ...... re status composition approach
@ast
Identification of real microRN ...... re status composition approach
@en
Identification of real microRN ...... re status composition approach
@nl
P2093
P2860
P3181
P1433
P1476
Identification of real microRN ...... re status composition approach
@en
P2093
Junjie Chen
Longyun Fang
Xiaolong Wang
P2860
P304
P3181
P356
10.1371/JOURNAL.PONE.0121501
P407
P577
2015-01-01T00:00:00Z