Applications of machine Learning to improve the efficiency and range of
microbial biosynthesis: a review of state-of-art techniques
- URL: http://arxiv.org/abs/2308.13877v2
- Date: Sat, 14 Oct 2023 23:27:42 GMT
- Title: Applications of machine Learning to improve the efficiency and range of
microbial biosynthesis: a review of state-of-art techniques
- Authors: Akshay Bhalla, Suraj Rajendran
- Abstract summary: This paper provides a comprehensive overview of the differing machine learning programs used in biosynthesis.
It also highlights challenges and research directions, acting to instigate more research and development in the growing fields.
The paper aims to act as a reference for academics performing research, industry professionals improving their processes, and students looking to understand the concept of machine learning in biosynthesis.
- Score: 1.0878040851638
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the modern world, technology is at its peak. Different avenues in
programming and technology have been explored for data analysis, automation,
and robotics. Machine learning is key to optimize data analysis, make accurate
predictions, and hasten/improve existing functions. Thus, presently, the field
of machine learning in artificial intelligence is being developed and its uses
in varying fields are being explored. One field in which its uses stand out is
that of microbial biosynthesis. In this paper, a comprehensive overview of the
differing machine learning programs used in biosynthesis is provided, alongside
brief descriptions of the fields of machine learning and microbial biosynthesis
separately. This information includes past trends, modern developments, future
improvements, explanations of processes, and current problems they face. Thus,
this paper's main contribution is to distill developments in, and provide a
holistic explanation of, 2 key fields and their applicability to improve
industry/research. It also highlights challenges and research directions,
acting to instigate more research and development in the growing fields.
Finally, the paper aims to act as a reference for academics performing
research, industry professionals improving their processes, and students
looking to understand the concept of machine learning in biosynthesis.
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