ArticleScientific reports2017
Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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.
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Who cites it
25 citing papers in PubMed, 47 citations in OpenAlex.
- Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.International journal of radiation oncology, biology, physics · 2018Trial
- Utilization of artificial intelligence in Men's Health: Opportunities for innovation and quality improvement.International journal of impotence research · 2026Review
- Predictive Factors for Gastrointestinal and Genitourinary Toxicities in Prostate Cancer External Beam Radiotherapy: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Towards Personalized Radiotherapy in Pelvic Cancer: Patient-Related Risk Factors for Late Radiation Toxicity.Current oncology (Toronto, Ont.) · 2025Review
- Artificial Intelligence-Based Clinical Decision-Making in Erectile Dysfunction: a Narrative Review.Current urology reports · 2024Review
- Development and validation of a prediction model for ED using machine learning: according to NHANES 2001-2004.Scientific reports · 2024Article
- Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making.Seminars in radiation oncology · 2024Review
- Lipid metabolism and neuromuscular junction as common pathways underlying the genetic basis of erectile dysfunction and obstructive sleep apnea.International journal of impotence research · 2024Article
- Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Article
- Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review.International journal of impotence research · 2023Review
- Development and external validation of multivariate prediction models for erectile dysfunction in men with localized prostate cancer.PloS one · 2023Article
- Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.Frontiers in plant science · 2023Review
- Identification of Novel Regulators of Radiosensitivity Using High-Throughput Genetic Screening.International journal of molecular sciences · 2022Review
- Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2021Article
- Identification of biological correlates associated with respiratory failure in COVID-19.BMC medical genomics · 2020Article
- Harnessing genome-wide association studies to minimize adverse radiation-induced side effects.Radiation oncology journal · 2020Article
- Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data.JNCI cancer spectrum · 2020Article
- Genomics models in radiotherapy: From mechanistic to machine learning.Medical physics · 2020Review
- Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy.Frontiers in oncology · 2020Review
- Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.
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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.