ArticleScientific reports2016
Large-Scale Discovery of Disease-Disease and Disease-Gene Associations.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- Genetic and Genomic Testing in Cardiovascular Disease: A Policy Statement From the American Heart Association.Circulation · 2025Review
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Exploring novel disease-disease associations based on multi-view fusion network.Computational and structural biotechnology journal · 2023Article
- Regulome-based characterization of drug activity across the human diseasome.NPJ systems biology and applications · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset.Scientific reports · 2021Article
- Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood.Entropy (Basel, Switzerland) · 2021Article
- Use of disease embedding technique to predict the risk of progression to end-stage renal disease.Journal of biomedical informatics · 2020Article
- Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining.eLife · 2019Article
- Optimizing clinical trials recruitment via deep learning.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm.Bioinformatics (Oxford, England) · 2019Article
- Reforming disease classification system-are we there yet?Annals of translational medicine · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Autism genetics: opportunities and challenges for clinical translation.Nature reviews. Genetics · 2017Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven phenotype analyses on Electronic Health Record (EHR) data have recently drawn benefits across many areas of clinical practice, uncovering new links in the medical sciences that can potentially affect the well-being of millions of patients. In this paper, EHR data is used to discover novel relationships between diseases by studying their comorbidities (co-occurrences in patients). A novel embedding model is designed to extract knowledge from disease comorbidities by learning from a large-scale EHR database comprising more than 35 million inpatient cases spanning nearly a decade, revealing significant improvements on disease phenotyping over current computational approaches. In addition, the use of the proposed methodology is extended to discover novel disease-gene associations by including valuable domain knowledge from genome-wide association studies. To evaluate our approach, its effectiveness is compared against a held-out set where, again, it revealed very compelling results. For selected diseases, we further identify candidate gene lists for which disease-gene associations were not studied previously. Thus, our approach provides biomedical researchers with new tools to filter genes of interest, thus, reducing costly lab studies.
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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.