ArticleScientific reports2022
A review of some techniques for inclusion of domain-knowledge into deep neural networks.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps.Radiological physics and technology · 2026Review
- Deep learning for incidence rate prediction and radiation risk assessment of solid tumors.Scientific reports · 2026Article
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Multitask deep learning for the emulation and calibration of an agent-based malaria transmission model.PLoS computational biology · 2025Article
- Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.Radiology. Artificial intelligence · 2024Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- 3D molecular generative framework for interaction-guided drug design.Nature communications · 2024Article
- Multi-omics based artificial intelligence for cancer research.Advances in cancer research · 2024Review
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study.Children (Basel, Switzerland) · 2023Article
- ArkDTA: attention regularization guided by non-covalent interactions for explainable drug-target binding affinity prediction.Bioinformatics (Oxford, England) · 2023Article
- High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information.Biomedicines · 2023Article
- Hyperthermia Treatment Monitoring via Deep Learning Enhanced Microwave Imaging: A Numerical Assessment.Cancers · 2023Article
- An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications.Sensors (Basel, Switzerland) · 2023Article
- Ki-Cook: clustering multimodal cooking representations through knowledge-infused learning.Frontiers in big data · 2023Article
- Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.Frontiers in oncology · 2023Article
- Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks.Plant phenomics (Washington, D.C.) · 2023Article
- Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare.Cell reports. Medicine · 2022Review
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.
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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.