Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
about
Cytotoxic and targeted therapy for hereditary cancersDeep learning for computational biology.DISIS: prediction of drug response through an iterative sure independence screeningPredicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network ModelA Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity PredictionA survey of current trends in computational drug repositioningInferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic propertiesA community effort to assess and improve drug sensitivity prediction algorithms.Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis.StickWRLD as an Interactive Visual Pre-Filter for Canceromics-Centric Expression Quantitative Trait Locus Data.Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain.How Consistent are Publicly Reported Cytotoxicity Data? Large-Scale Statistical Analysis of the Concordance of Public Independent Cytotoxicity Measurements.Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell linesDrug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learningCombinative in vitro studies and computational model to predict 3D cell migration response to drug insultSensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functionsPrediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression.Virtual screening of chemical compounds active against breast cancer cell lines based on cell cycle modelling, prediction of cytotoxicity and interaction with targets.Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.ksRepo: a generalized platform for computational drug repositioningCoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network.Mining the Breast Cancer Proteome for Predictors of Drug Sensitivity.Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks.Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data.Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel.Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.Use of a Combined Gene Expression Profile in Implementing a Drug Sensitivity Predictive Model for Breast Cancer.Automated Classification of Benign and Malignant Proliferative Breast LesionsImproved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization.Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization.Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine.Computational models for predicting drug responses in cancer research.Current Trends in Multidrug Optimization.Multitask learning improves prediction of cancer drug sensitivity.Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs.Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.Predicting ligand-dependent tumors from multi-dimensional signaling features.A link prediction approach to cancer drug sensitivity prediction.Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression.
P2860
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P2860
Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
description
2013 nî lūn-bûn
@nan
2013 թուականին հրատարակուած գիտական յօդուած
@hyw
2013 թվականին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
Machine learning prediction of ...... enomic and chemical properties
@ast
Machine learning prediction of ...... enomic and chemical properties
@en
Machine learning prediction of ...... enomic and chemical properties
@nl
type
label
Machine learning prediction of ...... enomic and chemical properties
@ast
Machine learning prediction of ...... enomic and chemical properties
@en
Machine learning prediction of ...... enomic and chemical properties
@nl
prefLabel
Machine learning prediction of ...... enomic and chemical properties
@ast
Machine learning prediction of ...... enomic and chemical properties
@en
Machine learning prediction of ...... enomic and chemical properties
@nl
P2093
P2860
P50
P3181
P1433
P1476
Machine learning prediction of ...... enomic and chemical properties
@en
P2093
Cyril H Benes
Mathew Garnett
Michael P Menden
P2860
P304
P3181
P356
10.1371/JOURNAL.PONE.0061318
P407
P577
2013-01-01T00:00:00Z