ReviewNPJ precision oncology2017
Network-based machine learning and graph theory algorithms for precision oncology.
Review in NPJ precision oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 124 citations in OpenAlex.
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.Bioinformatics advances · 2026Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics.JMIR bioinformatics and biotechnology · 2025Article
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- "Polytopal Rearrangement Model of Stereoisomerization" and Its Potential as the Basis for a Systematic Model of All Stereoisomerism.ACS organic & inorganic Au · 2024Review
- Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.Briefings in bioinformatics · 2024Article
- GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network.Bioinformatics (Oxford, England) · 2024Article
- Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations.ACS omega · 2024Review
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
- Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.PloS one · 2024Article
- Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications: A Network-Based Approach.Bioinformatics and biology insights · 2024Article
- Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Brain tumour genetic network signatures of survival.Brain : a journal of neurology · 2023Article
- PLK1 as a cooperating partner for BCL2-mediated antiapoptotic program in leukemia.Blood cancer journal · 2023Article
- Integrating multiomics and prior knowledge: a study of the Graphnet penalty impact.Bioinformatics (Oxford, England) · 2023Article
- Review
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Machine Learning of Multi-Modal Tumor Imaging Reveals Trajectories of Response to Precision Treatment.Cancers · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.