ReviewIEEE reviews in biomedical engineering2012
Multiscale integration of -omic, imaging, and clinical data in biomedical informatics.
Review in IEEE reviews in biomedical engineering, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- StainStyleSampler: clustering-based sampling of whole slide image appearances.Diagnostic pathology · 2026Article
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.Journal of translational medicine · 2025Review
- Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.Radiology. Artificial intelligence · 2024Article
- SaBrcada: Survival Intervals Prediction for Breast Cancer Patients by Dimension Raising and Age Stratification.Cancers · 2023Article
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.Journal of medical Internet research · 2021Article
- Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis.BMC medical informatics and decision making · 2020Article
- A U-statistics for integrative analysis of multilayer omics data.Bioinformatics (Oxford, England) · 2020Article
- Platform for quantitative multiscale imaging of tissue composition.Biomedical optics express · 2020Article
- Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data.BMC medical informatics and decision making · 2020Article
- Improving Classification of Breast Cancer by Utilizing the Image Pyramids of Whole-Slide Imaging and Multi-Scale Convolutional Neural Networks.Proceedings : Annual International Computer Software and Applications Conference. COMPSAC · 2019Article
- An integrative association method for omics data based on a modified Fisher's method with application to childhood asthma.PLoS genetics · 2019Article
- A conceptual framework for evaluating data suitability for observational studies.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- An Assessment of Imaging Informatics for Precision Medicine in Cancer.Yearbook of medical informatics · 2017Review
- An Introduction to Programming for Bioscientists: A Python-Based Primer.PLoS computational biology · 2016Article
- Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies.BMC bioinformatics · 2015Article
- Integrative network modeling approaches to personalized cancer medicine.Personalized medicine · 2015Article
- Region Templates: Data Representation and Management for High-Throughput Image Analysis.Parallel computing · 2014Article
- "Big data" and the electronic health record.Yearbook of medical informatics · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.
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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.