Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest
about
Sequence-based prediction of protein protein interaction using a deep-learning algorithm.Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding.Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks.PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein SequencesIdentification of 14-3-3 Proteins Phosphopeptide-Binding Specificity Using an Affinity-Based Computational Approach.Predicting protein-protein interactions via multivariate mutual information of protein sequences.Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selectionPredicting protein-binding regions in RNA using nucleotide profiles and compositions.RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1Identification of Protein-Protein Interactions via a Novel Matrix-Based Sequence Representation Model with Amino Acid Contact Information.An improved method for predicting interactions between virus and human proteins.Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree.Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network.Review and comparative assessment of sequence-based predictors of protein-binding residues.Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.Prediction of Protein-Protein Interactions.Different protein-protein interface patterns predicted by different machine learning methods.SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome.Prediction of cassava protein interactome based on interolog method.PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.Deep Neural Network Based Predictions of Protein Interactions Using Primary SequencesIntegration of multiple types of genetic markers for neuroblastoma may contribute to improved prediction of the overall survivalPPInS: a repository of protein-protein interaction sitesbase
P2860
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P2860
Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest
description
2015 nî lūn-bûn
@nan
2015 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի մայիսին 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
Predicting protein-protein int ...... n scheme and the random forest
@ast
Predicting protein-protein int ...... n scheme and the random forest
@en
type
label
Predicting protein-protein int ...... n scheme and the random forest
@ast
Predicting protein-protein int ...... n scheme and the random forest
@en
prefLabel
Predicting protein-protein int ...... n scheme and the random forest
@ast
Predicting protein-protein int ...... n scheme and the random forest
@en
P2860
P1433
P1476
Predicting protein-protein int ...... n scheme and the random forest
@en
P2093
Pengwei Hu
Zhu-Hong You
P2860
P304
P356
10.1371/JOURNAL.PONE.0125811
P407
P50
P577
2015-05-06T00:00:00Z