{
  "id": "mapping-biomaterial-complexity-machine-learning",
  "type": "publication",
  "status": "published",
  "title": "Mapping Biomaterial Complexity by Machine Learning",
  "authors": [
    "Eman Ahmed",
    "Prajakatta Mulay",
    "Cesar Ramirez",
    "Gabriela Tirado-Mansilla",
    "Eugene Cheong",
    "Adam J. Gormley"
  ],
  "authorPosition": "first",
  "publicationType": "Review",
  "year": "2024",
  "date": "2024-10-11",
  "venue": "Tissue Engineering Part A",
  "volume": "30",
  "issue": "19-20",
  "pages": "662-680",
  "doi": "10.1089/ten.tea.2024.0067",
  "doiUrl": "https://doi.org/10.1089/ten.tea.2024.0067",
  "pmid": "39135398",
  "pmcid": "PMC12394815",
  "openAccessUrl": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12394815/",
  "citationCount": "25",
  "keywords": [
    "biomaterials",
    "high-throughput experimentation",
    "machine learning",
    "structure-property relationships",
    "tissue engineering",
    "data mining"
  ],
  "summary": "Biomaterials rarely fail or succeed for one obvious reason. Performance usually comes from a combination of small structural details interacting at once. Testing those combinations one experiment at a time does not scale. This review sets out how high-throughput experimentation paired with machine learning changes the search: run many conditions in parallel, keep every data point including the failures, and train models that map structure to function across the whole space rather than at a few sampled points.",
  "url": "https://emanahmed.org/publications/mapping-biomaterial-complexity-machine-learning.html",
  "apiUrl": "https://emanahmed.org/api/publications/mapping-biomaterial-complexity-machine-learning.json",
  "abstract": "Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure–function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure–function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.",
  "describedBy": "https://emanahmed.org/openapi.json",
  "citations": {
    "bibtex": "@article{ahmed2024mapping,\n  title   = {Mapping Biomaterial Complexity by Machine Learning},\n  author  = {Ahmed, Eman and Mulay, Prajakatta and Ramirez, Cesar and Tirado-Mansilla, Gabriela and Cheong, Eugene and Gormley, Adam J.},\n  journal = {Tissue Engineering Part A},\n  volume  = {30},\n  number  = {19-20},\n  pages   = {662-680},\n  year    = {2024},\n  doi     = {10.1089/ten.tea.2024.0067}\n}",
    "apa": "Ahmed, E., Mulay, P., Ramirez, C., Tirado-Mansilla, G., Cheong, E. & Gormley, A. J. (2024). Mapping Biomaterial Complexity by Machine Learning. Tissue Engineering Part A, 30(19-20), 662-680. https://doi.org/10.1089/ten.tea.2024.0067"
  },
  "license": {
    "content": "CC-BY-4.0",
    "contentUrl": "https://creativecommons.org/licenses/by/4.0/",
    "note": "Site prose and this API's metadata may be reused with attribution to Eman Ahmed (emanahmed.org). Published abstracts are reproduced from the publisher of record named in each record's `doi`; their reuse is governed by that publisher, not by this licence.",
    "attribution": "Eman Ahmed, https://emanahmed.org"
  },
  "provenance": {
    "source": "Generated from the site's single source of truth at build time. Bibliographic fields were taken from the publisher of record or PubMed Central, not from memory.",
    "citationMetricsAsOf": "September 2026",
    "canonical": "https://emanahmed.org/"
  }
}