ArticleCell reports2021
Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.
Article in Cell reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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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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
15 citing papers in PubMed, 29 citations in OpenAlex.
- Integrated multi-omic and symptom clustering reveals lower-gastrointestinal disorders of gut-brain interaction heterogeneity.Gut microbes · 2026Article
- Navigating multi-omic integration methods for human microbiome research.Nature microbiology · 2026Review
- Research Progress on Construction Technology of 3D Human Skin Models and Its Application Prospects in Dermatology.International journal of molecular sciences · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Multiomic approaches for endotype discovery in allergy/immunology.The Journal of allergy and clinical immunology · 2025Article
- Robust self-supervised machine learning for single cell embeddings and annotations.bioRxiv : the preprint server for biology · 2025Article
- EMitool: Explainable Multi-Omics Integration for Disease Subtyping.International journal of molecular sciences · 2025Article
- Microbial influencers: the airway microbiome's role in asthma.The Journal of clinical investigation · 2025Review
- Asthma Pathogenesis: Phenotypes, Therapies, and Gaps: Summary of the Aspen Lung Conference 2023.American journal of respiratory cell and molecular biology · 2024Review
- Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies.Osteoarthritis and cartilage · 2024Review
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- 'Multi-omics' data integration: applications in probiotics studies.NPJ science of food · 2023Review
- Multi-Omics Endotypes in ICU Sepsis-Induced Immunosuppression.Microorganisms · 2023Review
- The Role of Systems Biology in Deciphering Asthma Heterogeneity.Life (Basel, Switzerland) · 2022Review
- Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma.Journal of personalized medicine · 2021Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-free, automated pipeline enabling integration of categorical and numeric data spanning clinical and multi-omic profiles for unsupervised clustering to identify disease subsets. Using simulations and real-world data from The Cancer Genome Atlas, we demonstrate that MANAclust's feature selection algorithms are accurate and outperform competitors. We also apply MANAclust to a clinically and multi-omically phenotyped asthma cohort. MANAclust identifies clinically and molecularly distinct clusters, including heterogeneous groups of "healthy controls" and viral and allergy-driven subsets of asthmatic subjects. We also find that subjects with similar clinical presentations have disparate molecular profiles, highlighting the need for additional testing to uncover asthma endotypes. This work facilitates data-driven personalized medicine through integration of clinical parameters with multi-omics. MANAclust is freely available at https://bitbucket.org/scottyler892/manaclust/src/master/.
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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.