ReviewCurrent opinion in biotechnology2020
Incorporating biological structure into machine learning models in biomedicine.
Review in Current opinion in biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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
20 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
- Special Issue "Machine Learning and Bioinformatics in Human Health and Disease 2nd Edition".International journal of molecular sciences · 2026Article
- Synthesizing Mechanistic Hypotheses from Single-Cell Omics via Discretized Feature Attribution and Empirical Language Model Grounding.bioRxiv : the preprint server for biology · 2026Article
- GRACKLE: an interpretable matrix factorization approach for biomedical representation learning.Bioinformatics (Oxford, England) · 2025Article
- Optimizing Model Performance and Interpretability: Application to Biological Data Classification.Genes · 2025Article
- Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection.Translational oncology · 2025Article
- Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.Journal of chemical information and modeling · 2025Article
- Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data.BioData mining · 2024Article
- Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.Cell systems · 2024Article
- DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.Frontiers in genetics · 2024Article
- Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes.Briefings in bioinformatics · 2023Article
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing?Journal of personalized medicine · 2022Article
- Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data.BioData mining · 2022Article
- miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning.Frontiers in genetics · 2022Article
- MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Networks, Inspired by Capsule Networks.NPJ systems biology and applications · 2021Article
- Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis.BioData mining · 2021Article
- Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.Frontiers in cellular and infection microbiology · 2021Article
- Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.Nature communications · 2020Article
- Seven myths of how transcription factors read the cis-regulatory code.Current opinion in systems biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexpression networks, ontologies, and phylogenetic trees. We highlight recent examples of machine learning models that use structure to constrain model architecture or incorporate structured data into model training. For machine learning in biomedicine, where sample size is limited and model interpretability is crucial, incorporating prior knowledge in the form of structured data can be particularly useful. The area of research would benefit from performant open source implementations and independent benchmarking efforts.
Indexed as
Identifiers
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.