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Biotechnology, Big Data and Artificial Intelligence.

Arlindo L Oliveira1

  • 1INESC-ID, Instituto Superior Técnico, University of Lisbon, R. Alves Redol 9, 1000-029, Lisboa, Portugal.

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|March 31, 2019
PubMed
Summary
This summary is machine-generated.

Biotechnology relies heavily on big data and artificial intelligence. Mastering data integration and exploitation is crucial for future advancements in areas like drug discovery and genomics.

Keywords:
artificial intelligencebig databioengineeringmachine learning

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Area of Science:

  • Biotechnology and Data Science
  • Bioinformatics
  • Computational Biology

Background:

  • Modern biotechnology generates vast amounts of data from high-throughput technologies.
  • This data is stored across numerous public and private databases.
  • Effective utilization of this big data is essential for future research.

Purpose of the Study:

  • To explore the relationship between big data and biotechnology.
  • To highlight the roles of artificial intelligence and machine learning.
  • To outline key steps for future biotechnology projects.

Main Methods:

  • Literature review and conceptual analysis.
  • Discussion of data integration, exploitation, and optimization.
  • Identification of key application areas.

Main Results:

  • Big data, AI, and machine learning are integral to modern biotechnology.
  • Data integration, exploitation, and process optimization are critical steps.
  • Key applications include drug discovery, genomics, and proteomics.

Conclusions:

  • Biotechnology's future depends on researchers' ability to leverage big data.
  • Proficiency in data science skills is becoming a prerequisite for biotechnologists.
  • Strategic data management and analysis will drive innovation across various biotech fields.