iRSpot-EL: identify recombination spots with an ensemble learning approach.
about
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 networkDevelopment of machine learning models for diagnosis of glaucoma.Predicting human protein subcellular localization by heterogeneous and comprehensive approaches.Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components.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.iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covarianceIdentification 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.iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals.Deep learning approach to bacterial colony classification.iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide compositionSmall molecular floribundiquinone B derived from medicinal plants inhibits acetylcholinesterase activity.Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC.Sequence-based predictive modeling to identify cancerlectins.Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine.iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences.2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.Taxonomic Classification for Living Organisms Using Convolutional Neural Networks.MLACP: machine-learning-based prediction of anticancer peptides.Distributed smoothed tree kernel for protein-protein interaction extraction from the biomedical literature.Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound.Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches.Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate.pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites.iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC.iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.Prediction of protein-protein interactions between fungus (Magnaporthe grisea) and rice (Oryza sativa L.).Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.Ensemble learning method for the prediction of new bioactive molecules.Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers.
P2860
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P2860
iRSpot-EL: identify recombination spots with an ensemble learning approach.
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年学术文章
@wuu
2016年学术文章
@zh-cn
2016年学术文章
@zh-hans
2016年学术文章
@zh-my
2016年学术文章
@zh-sg
2016年學術文章
@yue
2016年學術文章
@zh
2016年學術文章
@zh-hant
name
iRSpot-EL: identify recombination spots with an ensemble learning approach.
@en
iRSpot-EL: identify recombination spots with an ensemble learning approach.
@nl
type
label
iRSpot-EL: identify recombination spots with an ensemble learning approach.
@en
iRSpot-EL: identify recombination spots with an ensemble learning approach.
@nl
prefLabel
iRSpot-EL: identify recombination spots with an ensemble learning approach.
@en
iRSpot-EL: identify recombination spots with an ensemble learning approach.
@nl
P2093
P2860
P356
P1433
P1476
iRSpot-EL: identify recombination spots with an ensemble learning approach.
@en
P2093
P2860
P356
10.1093/BIOINFORMATICS/BTW539
P407
P577
2016-08-16T00:00:00Z