ArticleScientific reports2021
Pattern discovery and disentanglement on relational datasets.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Integrative machine learning analysis suggests novel molecular targets for liver cancer diagnosis and therapy.Discover oncology · 2026Article
- Benchmarking Interpretability in Healthcare Using Pattern Discovery and Disentanglement.Bioengineering (Basel, Switzerland) · 2025Article
- An Unsupervised Error Detection Methodology for Detecting Mislabels in Healthcare Analytics.Bioengineering (Basel, Switzerland) · 2024Article
- AI illuminates paths in oral cancer: transformative insights, diagnostic precision, and personalized strategies.EXCLI journal · 2024Review
- Theory and rationale of interpretable all-in-one pattern discovery and disentanglement system.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.
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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.