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Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine

https://doi.org/10.1101/331769
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Characterization of the prostate cancer susceptibility gene KLF6 in human and mouse prostate cancers
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miR-429 inhibits cells growth and invasion and regulates EMT-related marker genes by targeting Onecut2 in colorectal carcinoma
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The LIM domain protein, CRIP2, promotes apoptosis in esophageal squamous cell carcinoma
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A multicenter phase II study of sorafenib in combination with erlotinib in patients with advanced non-small cell lung cancer (KCSG-0806)
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Environment dominates over host genetics in shaping human gut microbiota
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Rationalizing combination therapies
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SCENIC: single-cell regulatory network inference and clustering
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Combine and conquer: challenges for targeted therapy combinations in early phase trials
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EMP2 regulates angiogenesis in endometrial cancer cells through induction of VEGF
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
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Kruppel-like factor 6 (KLF6) is a tumor-suppressor gene frequently inactivated in colorectal cancer☆
inferredcites →https://doi.org/10.1056/nejm197305102881905openalex-snapshot
The Use of Drugs in Combination for the Treatment of Cancer
inferredcites →https://doi.org/10.1073/pnas.1101747108openalex-snapshot
Cysteine-rich intestinal protein 2 ( CRIP2 ) acts as a repressor of NF-κB–mediated proangiogenic cytokine transcription to suppress tumorigenesis and angiogenesis
inferredcites →https://doi.org/10.1093/bioinformatics/btv080openalex-snapshot
Large-scale exploration and analysis of drug combinations
inferredcites →https://doi.org/10.1093/bioinformatics/btx806openalex-snapshot
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
inferredcites →https://doi.org/10.1093/nar/gkw1138openalex-snapshot
PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements
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KLF6-SV1 Drives Breast Cancer Metastasis and Is Associated with Poor Survival
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Kruppel-Like Factor 6 Is Frequently Down-Regulated and Induces Apoptosis in Non-Small Cell Lung Cancer Cells
inferredcites →https://doi.org/10.1158/0008-5472.can-06-1888openalex-snapshot
MIRA-Assisted Microarray Analysis, a New Technology for the Determination of DNA Methylation Patterns, Identifies Frequent Methylation of Homeodomain-Containing Genes in Lung Cancer Cells
inferredcites →https://doi.org/10.1158/1535-7163.mct-15-0843openalex-snapshot
An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies
inferredcites →https://doi.org/10.1158/1538-7445.am2018-3886openalex-snapshot
Abstract 3886: A large cancer pharmacogenomics combination screen powering crowd-sourced advancement of computational drug synergy predictions
inferredcites →https://doi.org/10.1177/0300060513499092openalex-snapshot
Krüppel-like factor 6 in the progression and prognosis of malignant melanoma
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Approaches to modernize the combination drug development paradigm
inferredcites →https://doi.org/10.18632/oncotarget.7327openalex-snapshot
CRP2, a new invadopodia actin bundling factor critically promotes breast cancer cell invasion and metastasis
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On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1603.02754openalex-snapshot
XGBoost: A Scalable Tree Boosting System
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.48550/arxiv.1802.03888openalex-snapshot
Consistent Individualized Feature Attribution for Tree Ensembles

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Enhancement of Drug Synergy Model for Malignant Diseases Using Parameter Optimization
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Machine Learning Applications in Anti-cancer Drug Discovery
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Hybrid Predictive Modelling for Finding Optimal Multipurpose Multicomponent Therapy
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Robust Alignment and Cell Tracking Pipeline for Multiplex IHC in FFPE Tissue Sections
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A Systematic Review of Applications of Machine Learning in Cancer Prediction and Diagnosis
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DALI-Syn: integrating chemical LLMs with multi-level attention architectures for synergistic drug combination prediction
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An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms
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PerturbSynX: Deep learning framework for predicting drug combination synergy scores using drug induced gene perturbation data
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How to predict effective drug combinations – moving beyond synergy scores
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Hybrid genetic predictive modeling for finding optimal multipurpose multicomponent therapy
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Exact Shapley values for local and model-true explanations of decision tree ensembles
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Machine learning for an explainable cost prediction of medical insurance
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Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices
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Probabilistic graphical models for finding optimal multipurpose multicomponent therapy
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A Stacked Ensemble Approach For Enhancing Anti Cancer Drug Synergy Prediction
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2018.10.368openalex-snapshot
Explainable extreme gradient boosting tree-based prediction of toluene, ethylbenzene and xylene wet deposition
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GraFSyn: An Interpretable Deep Learning Framework for Anticancer Drug Synergy via Graphlet Fingerprints
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Building a unified model for drug synergy analysis powered by large language models
inferred← citeshttps://doi.org/10.1038/s41598-024-62956-6openalex-snapshot
Reliable anti-cancer drug sensitivity prediction and prioritization
inferred← citeshttps://doi.org/10.1038/s41598-025-02444-7openalex-snapshot
Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways
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A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
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Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects
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Machine learning methods, databases and tools for drug combination prediction
inferred← citeshttps://doi.org/10.1093/bib/bbab562openalex-snapshot
An enhanced cascade-based deep forest model for drug combination prediction
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A review of machine learning approaches for drug synergy prediction in cancer
inferred← citeshttps://doi.org/10.1093/bib/bbae379openalex-snapshot
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
inferred← citeshttps://doi.org/10.1093/bioinformatics/btad177openalex-snapshot
MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores
inferred← citeshttps://doi.org/10.1093/bioinformatics/btae134openalex-snapshot
From cell lines to cancer patients: personalized drug synergy prediction
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CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations
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Dual-view jointly learning improves personalized drug synergy prediction
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Advancing personalized cancer therapy: Onko_DrugCombScreen—a novel Shiny app for precision drug combination screening
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RWRDC: Predicting Efficacious Drug Combinations in Cancer Based on Random Walk with Restart
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PathPCNet: Pathway Principal Component-Based Interpretable Framework for Drug Sensitivity Prediction
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An efficient drug synergy model based on deep learning for malignant diseases
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MatchMaker: A Deep Learning Framework for Drug Synergy Prediction
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GNNSynergy: A Multi-View Graph Neural Network for Predicting Anti-Cancer Drug Synergy
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Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach
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Artificial Intelligence in Drug Treatment
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A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction
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Enhancing scientific discoveries in molecular biology with deep generative models
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SynPred: Prediction of Drug Combination Effects in Cancer using Full-Agreement Synergy Metrics and Deep Learning
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Five-Feature Model for Developing the Classifier for Synergistic vs. Antagonistic Drug Combinations Built by XGBoost
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TransferBAN-Syn: a transfer learning-based algorithm for predicting synergistic drug combinations against echinococcosis
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Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer
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Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
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Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China
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A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling
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Prediction of Drug Synergy by Ensemble Learning
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True to the Model or True to the Data?
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Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare
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Automated Decision Support System for Traumatic Injuries
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Anticancer drug synergy prediction based on CatBoost
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