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Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Using PDX for Biomarker Development
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Challenges and opportunities for oncology biomarker discovery
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Molecularly targeted cancer therapy: some lessons from the past decade
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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Gene Selection for Cancer Classification using Support Vector Machines
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Perspective: The precision-oncology illusion
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Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR
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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
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Systematic identification of genomic markers of drug sensitivity in cancer cells
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Orthotopic patient-derived xenografts of paediatric solid tumours
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A community effort to assess and improve drug sensitivity prediction algorithms
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Correlating chemical sensitivity and basal gene expression reveals mechanism of action
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Evaluating cell lines as tumour models by comparison of genomic profiles
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High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response
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The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
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Machine learning applications in genetics and genomics
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Erlotinib in Lung Cancer — Molecular and Clinical Predictors of Outcome
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Survival in BRAF V600–Mutant Advanced Melanoma Treated with Vemurafenib
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Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors
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A review of feature selection techniques in bioinformatics
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Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression
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Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics
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Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells
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An Integrated Approach to Anti-Cancer Drug Sensitivity Prediction
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Pharmacogenomic biomarkers for personalized cancer treatment
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A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data
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The Promise of Patient-Derived Xenografts: The Best Laid Plans of Mice and Men
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Comparing Patient-Derived Xenograft and Computational Response Prediction for Targeted Therapy in Patients of Early-Stage Large Cell Lung Cancer
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Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics
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Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics
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Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research
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AACR Project GENIE: Powering Precision Medicine through an International Consortium
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Bias in error estimation when using cross-validation for model selection
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Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines
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Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models
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Unearthing new genomic markers of drug response by improved measurement of discriminative power
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Assessing breast cancer cell lines as tumour models by comparison of mRNA expression profiles
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Phase II Trial (BREAK-2) of the BRAF Inhibitor Dabrafenib (GSK2118436) in Patients With Metastatic Melanoma
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Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data
inferredcites →https://doi.org/10.12688/f1000research.10529.2openalex-snapshot
Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data
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The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures
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Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties
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DISIS: Prediction of Drug Response through an Iterative Sure Independence Screening
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Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia
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An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts
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Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours
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Identifying anti-cancer drug response related genes using an integrative analysis of transcriptomic and genomic variations with cell line-based drug perturbations
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Gene Mutation Analysis in EGFR Wild Type NSCLC Responsive to Erlotinib: Are There Features to Guide Patient Selection?
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On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1038/s41598-019-50720-0openalex-snapshot
Modeling cancer drug response through drug-specific informative genes
inferred← citeshttps://doi.org/10.1093/bib/bbab312openalex-snapshot
A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling
inferred← citeshttps://doi.org/10.1186/s12885-024-12575-1openalex-snapshot
Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study
inferred← citeshttps://doi.org/10.1186/s13321-020-00444-5openalex-snapshot
QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction
inferred← citeshttps://doi.org/10.3389/fgene.2019.01041openalex-snapshot
Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data
inferred← citeshttps://doi.org/10.3390/biom10060963openalex-snapshot
Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles
inferred← citeshttps://doi.org/10.70675/df2ce30ez1a6bz4ba3z9798z284f80a7fa8dopenalex-snapshot
Prediction of tumour in vivo response to treatments using its molecular profiles via machine learning
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