iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition.
about
Identifying anticancer peptides by using improved hybrid compositionsEP-DNN: A Deep Neural Network-Based Global Enhancer Prediction AlgorithmIdentification of Multi-Functional Enzyme with Multi-Label ClassifierPredicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.Molecular classification of prostate adenocarcinoma by the integrated somatic mutation profiles and molecular networkPrediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components.iPTM-mLys: identifying multiple lysine PTM sites and their different types.Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models.iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids.Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.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.ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble ClassifieriRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covarianceRelapse-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.iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition.iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.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.iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree.iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals.iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition.Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition.iRSpot-EL: identify recombination spots with an ensemble learning approach.pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.Application of unsupervised analysis techniques to lung cancer patient data.Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach.iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide compositionPrediction of N-linked glycosylation sites using position relative features and statistical moments.
P2860
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P2860
iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年学术文章
@wuu
2015年学术文章
@zh-cn
2015年学术文章
@zh-hans
2015年学术文章
@zh-my
2015年学术文章
@zh-sg
2015年學術文章
@yue
2015年學術文章
@zh
2015年學術文章
@zh-hant
name
iEnhancer-2L: a two-layer pred ...... -tuple nucleotide composition.
@en
type
label
iEnhancer-2L: a two-layer pred ...... -tuple nucleotide composition.
@en
prefLabel
iEnhancer-2L: a two-layer pred ...... -tuple nucleotide composition.
@en
P2093
P2860
P356
P1433
P1476
iEnhancer-2L: a two-layer pred ...... -tuple nucleotide composition.
@en
P2093
P2860
P304
P356
10.1093/BIOINFORMATICS/BTV604
P407
P577
2015-10-17T00:00:00Z