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Enhancement of Drug Synergy Model for Malignant Diseases Using Parameter Optimization

https://doi.org/10.2139/ssrn.4813485
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inferredcites →https://doi.org/10.1002/wsbm.51openalex-extracted
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inferredcites →https://doi.org/10.1007/978-1-0716-0849-4_12openalex-extracted
Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models
inferredcites →https://doi.org/10.1007/978-3-030-15729-6_10openalex-extracted
Natural Language Processing
inferredcites →https://doi.org/10.1007/978-81-322-3972-7_19openalex-extracted
Natural Language Processing
inferredcites →https://doi.org/10.1016/bs.apcsb.2022.05.006openalex-extracted
From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?
inferredcites →https://doi.org/10.1016/j.ccell.2020.09.014openalex-extracted
Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells
inferredcites →https://doi.org/10.1016/j.pharmthera.2013.01.016openalex-extracted
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
inferredcites →https://doi.org/10.1016/j.trecan.2019.02.003openalex-extracted
Polytherapy and Targeted Cancer Drug Resistance
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Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories
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Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects
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A view on drug resistance in cancer
inferredcites →https://doi.org/10.1038/s41598-019-45236-6openalex-extracted
In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data
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DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations
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Predictive approaches for drug combination discovery in cancer
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Combenefit: an interactive platform for the analysis and visualization of drug combinations
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DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
inferredcites →https://doi.org/10.1101/2020.04.10.036491openalex-extracted
Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model
inferredcites →https://doi.org/10.1101/331769openalex-extracted
Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine
inferredcites →https://doi.org/10.1109/tcbb.2021.3086702openalex-extracted
MatchMaker: A Deep Learning Framework for Drug Synergy Prediction
inferredcites →https://doi.org/10.1136/gutjnl-2022-327736openalex-extracted
Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN
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Auto-WEKA
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Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm
inferredcites →https://doi.org/10.1158/0008-5472.can-18-0740openalex-extracted
Network Propagation Predicts Drug Synergy in Cancers
inferredcites →https://doi.org/10.1158/1535-7163.mct-15-0843openalex-extracted
An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies
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Predicting tumor cell line response to drug pairs with deep learning
inferredcites →https://doi.org/10.1371/journal.pcbi.1006752openalex-extracted
Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer
inferredcites →https://doi.org/10.1371/journal.pcbi.1008653openalex-extracted
TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations
inferredcites →https://doi.org/10.1613/jair.1.11854openalex-extracted
Benchmark and Survey of Automated Machine Learning Frameworks
inferredcites →https://doi.org/10.18632/oncotarget.11745openalex-extracted
Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination
inferredcites →https://doi.org/10.3389/fchem.2019.00509openalex-extracted
Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data
inferredcites →https://doi.org/10.3389/fgene.2020.01000openalex-extracted
A Machine Learning Method for Drug Combination Prediction
inferredcites →https://doi.org/10.3389/fphar.2022.1032875openalex-extracted
Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies
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Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening
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Searching for Activation Functions
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The Importance of Cancer Cell Lines as in vitro Models in Cancer Methylome Analysis and Anticancer Drugs Testing
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