ArticleBioinformatics (Oxford, England)2018
A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Automated inflammatory bowel disease detection using wearable bowel sound event spotting.Frontiers in digital health · 2025Article
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Pathway Activation Analysis for Pan-Cancer Personalized Characterization Based on Riemannian Manifold.International journal of molecular sciences · 2024Article
- Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease.Cell regeneration (London, England) · 2023Article
- Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning.Digestive diseases and sciences · 2022Article
- Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis.Biomolecules · 2022Article
- Detection and Classification of Colorectal Polyp Using Deep Learning.BioMed research international · 2022Article
- "Technology Proficiency" in Medical Education: Worthiness for Worldwide Wonderful Competency and Sophistication.Advances in medical education and practice · 2022Article
- Review
- Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.World journal of gastroenterology · 2021Review
- Article
- Review
- Pathway and network embedding methods for prioritizing psychiatric drugs.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Big data in IBD: a look into the future.Nature reviews. Gastroenterology & hepatology · 2019Review
- Evaluation of Pathway Activation for a Single Sample Toward Inflammatory Bowel Disease Classification.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Summary: Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to noise and frequently do not replicate in external validation sets. For complex, heterogeneous diseases, these classifiers are further limited by being unable to capture varying combinations of genes that lead to the same phenotype. Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms. In this work, we developed a novel pathway-based approach, PRObabilistic Pathway Score, which uses genes to calculate individualized pathway scores for classification. Unlike previous individualized pathway-based classification methods that use gene sets, we incorporate gene interactions using probabilistic graphical models to more accurately represent the underlying biology and achieve better performance. We apply our method to differentiate two similar complex diseases, ulcerative colitis (UC) and Crohn's disease (CD), which are the two main types of inflammatory bowel disease (IBD). Using five IBD datasets, we compare our method against four gene-based and four alternative pathway-based classifiers in distinguishing CD from UC. We demonstrate superior classification performance and provide biological insight into the top pathways separating CD from UC. Availability and Implementation: PROPS is available as a R package, which can be downloaded at http://simtk.org/home/props or on Bioconductor. Contact: rbaltman@stanford.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.