Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data.
about
Predicting drug-target interaction networks based on functional groups and biological featuresIntegrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screeningA systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological dataNeighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction PredictionDrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rankComputational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features.Predicting receptor-ligand pairs through kernel learning.Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner.Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework.Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines.Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.An efficient algorithm for de novo predictions of biochemical pathways between chemical compoundsLearning a peptide-protein binding affinity predictor with kernel ridge regression.Predicting target-ligand interactions using protein ligand-binding site and ligand substructures.Scalable prediction of compound-protein interactions using minwise hashingTargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.Supervised prediction of drug-target interactions using bipartite local models.Building a drug-target network and its applications.Similarity-based machine learning methods for predicting drug-target interactions: a brief review.Drug-target interaction prediction via chemogenomic space: learning-based methods.Chemical biology of compounds obtained from screening using disease models.Benchmarking a Wide Range of Chemical Descriptors for Drug-Target Interaction Prediction Using a Chemogenomic Approach.DINIES: drug-target interaction network inference engine based on supervised analysis.VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines.Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures.Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.Pred-binding: large-scale protein-ligand binding affinity prediction.
P2860
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P2860
Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data.
description
2007 nî lūn-bûn
@nan
2007 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Statistical prediction of prot ...... re and mass spectrometry data.
@ast
Statistical prediction of prot ...... re and mass spectrometry data.
@en
type
label
Statistical prediction of prot ...... re and mass spectrometry data.
@ast
Statistical prediction of prot ...... re and mass spectrometry data.
@en
prefLabel
Statistical prediction of prot ...... re and mass spectrometry data.
@ast
Statistical prediction of prot ...... re and mass spectrometry data.
@en
P2860
P356
P1433
P1476
Statistical prediction of prot ...... re and mass spectrometry data.
@en
P2093
Nobuyoshi Nagamine
Yasubumi Sakakibara
P2860
P304
P356
10.1093/BIOINFORMATICS/BTM266
P407
P577
2007-05-17T00:00:00Z