iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.
about
Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In SilicoIdentification of real microRNA precursors with a pseudo structure status composition approachIdentifying anticancer peptides by using improved hybrid compositionsResistance gene identification from Larimichthys crocea with machine learning techniques.Identification of Multi-Functional Enzyme with Multi-Label ClassifierIdentification of Damaging nsSNVs in HumanERCC2 Gene.Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.iPTM-mLys: identifying multiple lysine PTM sites and their different types.repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.Benchmark data for identifying N(6)-methyladenosine sites in the Saccharomyces cerevisiae genomeImproved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set.Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributesEnvironmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses.Comparison of genomic data via statistical distribution.Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.Predicting cancerlectins by the optimal g-gap dipeptides.Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.Combined sequence and sequence-structure based methods for analyzing FGF23, CYP24A1 and VDR genes.Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.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 ClassifierRelapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinomaPAI: Predicting adenosine to inosine editing sites by using pseudo nucleotide compositions.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.Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.Construction and analysis of dysregulated lncRNA-associated ceRNA network identified novel lncRNA biomarkers for early diagnosis of human pancreatic cancer.Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in HumanCharacterization of proteins in S. cerevisiae with subcellular localizations.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.Cell-Peptide Specific Interaction Can Inhibit Mycobacterium tuberculosis H37Rv Infection.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.
P2860
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P2860
iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.
description
2014 nî lūn-bûn
@nan
2014 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@ast
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@en
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@nl
type
label
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@ast
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@en
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@nl
prefLabel
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@ast
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@en
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@nl
P2093
P2860
P356
P1476
iPro54-PseKNC: a sequence-base ...... -tuple nucleotide composition.
@en
P2093
P2860
P304
12961-12972
P356
10.1093/NAR/GKU1019
P407
P577
2014-10-31T00:00:00Z