ReviewMaterials today. Bio2021
Biomaterials by design: Harnessing data for future development.
Review in Materials today. Bio, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Biomaterials for biomarker imaging and detection.Journal of advanced research · 2026Review
- Understanding Fabrication Variability in Core-Shell Soft Biomaterials Using Stochastic Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- Materiobiology in the omics era.Materials today. Bio · 2025Review
- Review
- Toward Sex-Specific Biomaterials Innovation: A Perspective.ACS biomaterials science & engineering · 2025Review
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Recent regulatory developments in EU Medical Device Regulation and their impact on biomaterials translation.Bioengineering & translational medicine · 2025Review
- Fixation Failure in Osteoporotic Bone: A Review of Complications and Outcomes.Indian journal of orthopaedics · 2025Review
- Rethinking Biomedical Titanium Alloy Design: A Review of Challenges from Biological and Manufacturing Perspectives.Advanced healthcare materials · 2025Review
- New materials and complications of prostheses in humans: situation in Spain.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2024Review
- Gold-Hydrogel Nanocomposites for High-Resolution Laser-Based 3D Printing of Scaffolds with SERS-Sensing Properties.ACS applied bio materials · 2024Article
- Tailor-Made Polysaccharides for Biomedical Applications.ACS applied bio materials · 2024Review
- Electroactive composite biofilms integrating Kombucha, Chlorella and synthetic proteinoid Proto-Brains.Royal Society open science · 2024Article
- AI energized hydrogel design, optimization and application in biomedicine.Materials today. Bio · 2024Review
- Synthetic living materials in cancer biology.Nature reviews bioengineering · 2023Article
- Unleashing the Power of Artificial Intelligence in Materials Design.Materials (Basel, Switzerland) · 2023Review
- A User's Guide to Machine Learning for Polymeric Biomaterials.ACS polymers Au · 2023Review
- Machine Learning in Tissue Engineering.Tissue engineering. Part A · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.
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.