ReviewMolecular oncology2021
Artificial intelligence in cancer research: learning at different levels of data granularity.
Review in Molecular oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
19 citing papers in PubMed.
- Review
- Using artificial intelligence to make feeding and management decisions in dairy herds.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Oncolytic viruses: advanced strategies in cancer therapy.Signal transduction and targeted therapy · 2026Review
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Review
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project.GigaScience · 2025Article
- Liquid biopsy for gastric cancer: Techniques, applications, and future directions.World journal of gastroenterology · 2024Review
- Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review.JMIR medical informatics · 2024Review
- The role of Hospital-Based Cancer Registries (HBCRs) as information systems in the delivery of evidence-based integrated cancer care: a scoping review.Health systems (Basingstoke, England) · 2024Article
- Building Prediction Models for 30-Day Readmissions Among ICU Patients Using Both Structured and Unstructured Data in Electronic Health Records.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2023Article
- Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors.Journal of clinical medicine · 2023Review
- Screening Programs for Breast Cancer: Toward Individualized, Risk-Adapted Strategies of Early Detection.Cancer treatment and research · 2023Article
- ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.Journal of animal science · 2022Review
- Machine learning algorithms to predict atypical metastasis of colorectal cancer patients after surgical resection.Frontiers in surgery · 2022Article
- Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Precision Oncology Beyond Genomics: The Future Is Here-It Is Just Not Evenly Distributed.Cells · 2021Review
- Data Integration Challenges for Machine Learning in Precision Medicine.Frontiers in medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.
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What OpenQuestion holds
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.