ReviewScience advances2024
Domain adaptation in small-scale and heterogeneous biological datasets.
Review in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Review
- A Systems Approach to Endophyte-Mediated Plant Holobiont and Microbiome Dynamics.Plants (Basel, Switzerland) · 2026Review
- A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome.Communications medicine · 2026Article
- Overcoming domain-specific challenges for artificial intelligence in abdominal oncology toward clinical translation.Discover oncology · 2026Review
- Enhancing fMRI Decoded Neurofeedback with Co-adaptive Training: Simulation and Proof-of-principle Evidence.Neuroinformatics · 2026Article
- Article
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
- Translating Multimodal Intelligence into Cardiac Diagnostics: A Critical Perspective on Large Language Model-Assisted Electrogram Interpretation.Anatolian journal of cardiology · 2025Article
- Toward the Best Generalizable Performance of Machine Learning in Modeling Omic and Clinical Data.Laboratory investigation; a journal of technical methods and pathology · 2025Article
- AutoML-Driven Soft Sensors for Real-Time Monitoring of Amino Acids in Mammalian Perfusion Cultures.Biotechnology and bioengineering · 2025Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Privacy-preserving federated unsupervised domain adaptation with application to age prediction from DNA methylation data.Bioinformatics (Oxford, England) · 2025Article
- How to develop good research questions.Nature human behaviour · 2025Article
- Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.Brain informatics · 2025Article
- AI for rapid identification of major butyrate-producing bacteria in rhesus macaques (Macaca mulatta).Animal microbiome · 2025Article
- Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models.Nature microbiology · 2025Article
- CKS2 Mediates Hepatocellular Carcinoma Recurrence After Hepatic Ischemia-Reperfusion Injury Related to M2 Macrophages.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine-learning models are key to modern biology, yet models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories due to both technical and biological differences. Domain adaptation, a type of transfer learning, alleviates this problem by aligning different datasets so that models can be applied across them. However, most state-of-the-art domain adaptation methods were designed for large-scale data such as images, whereas biological datasets are smaller and have more features, and these are also complex and heterogeneous. This Review discusses domain adaptation methods in the context of such biological data to inform biologists and guide future domain adaptation research. We describe the benefits and challenges of domain adaptation in biological research and critically explore some of its objectives, strengths, and weaknesses. We argue for the incorporation of domain adaptation techniques to the computational biologist's toolkit, with further development of customized approaches.
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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.