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Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials.

Bikramjit Basu1, N H Gowtham2, Yang Xiao3

  • 1Materials Research Center, Indian Institute of Science, Bangalore, India; Translational Center of Excellence on Biomaterials for Orthopedic and Dental applications, Indian Institute of Science, Bangalore 560012, India.

Acta Biomaterialia
|February 24, 2022
PubMed
Summary
This summary is machine-generated.

Biomaterialomics integrates multi-omics data and artificial intelligence (AI) for faster, cheaper biomaterial and implant development. This data science approach accelerates innovation for patient-specific implants and advanced manufacturing.

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Area of Science:

  • Biomaterials Science
  • Data Science
  • Artificial Intelligence

Background:

  • Conventional biomaterial development is slow and costly due to complex manufacturing and biocompatibility testing.
  • There is a critical need to accelerate the development of implantable biomaterials, implants, and biomedical devices.

Purpose of the Study:

  • Introduce and define 'biomaterialomics' as a data science-driven approach to biomaterial development.
  • Outline the integration of multi-omics data, high-dimensional analysis, and AI tools throughout the biomaterial pipeline.
  • Demonstrate the application of biomaterialomics for patient-specific implants, additive manufacturing, and bioelectronic medicine.

Main Methods:

  • Integration of multi-omics data and high-dimensional analysis with AI tools.
  • Utilizing high-throughput biocompatibility data and multiscale physics-based models.
  • Employing data science approaches, including metadata management, AI/Machine Learning (ML) algorithms, and uncertainty prediction.

Main Results:

  • Establishment of processing-structure-property (PSP) linkages through cross-disciplinary approaches.
  • Demonstration of biomaterialomics for patient-specific implants, additive manufacturing, and bioelectronic medicine.
  • Predicted clinical performance of fourth-generation biomaterials and implants using 'digital twins'.

Conclusions:

  • Biomaterialomics offers a systematic, quantitative framework for biomaterial innovation.
  • AI/ML tools show great adaptability in biomaterials science.
  • Training researchers in data science is crucial for advancing biomaterials innovation.