Biotechnology, Big Data and Artificial Intelligence
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- TL;DR
- Modern biotechnology developments increasingly rely on big data generated by high-throughput instruments and stored across thousands of public and private databases.
- Problem
- Not specified in the abstract.
- Method
- Not specified in the abstract.
- Results
- Not specified in the abstract.
- Contributions
- Future progress depends critically on researchers' ability to integrate their own work with the massive amounts of information available in biological databases.
- Limitations
- Not specified in the abstract.
- Takeaways
- The article outlines the relationship between big data, artificial intelligence, and machine learning, and highlights data integration, exploitation, and process optimization as three essential steps for future biotechnology projects.
- Applications
- Key application areas include drug discovery, drug recycling, drug safety, functional and structural genomics, proteomics, pharmacogenetics, and pharmacogenomics.
- Topics
- Biotechnology; Big Data; Artificial Intelligence; Machine Learning
- For industry
- Biotechnology and pharmaceuticals.
- Why it matters
- Advances methods that could improve data integration and process optimization across biotechnology and genomics.
Abstract
Developments in biotechnology are increasingly dependent on the extensive use of big data, generated by modern high-throughput instrumentation technologies, and stored in thousands of databases, public and private. Future developments in this area depend, critically, on the ability of biotechnology researchers to master the skills required to effectively integrate their own contributions with the large amounts of information available in these databases. This article offers a perspective of the relations that exist between the fields of big data and biotechnology, including the related technologies of artificial intelligence and machine learning and describes how data integration, data exploitation, and process optimization correspond to three essential steps in any future biotechnology project. The article also lists a number of application areas where the ability to use big data will become a key factor, including drug discovery, drug recycling, drug safety, functional and structural genomics, proteomics, pharmacogenetics, and pharmacogenomics, among others.