Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F20%3A00534018" target="_blank" >RIV/86652079:_____/20:00534018 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26210/20:PU139928
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0734975020301336?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0734975020301336?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.biotechadv.2020.107631" target="_blank" >10.1016/j.biotechadv.2020.107631</a>
Alternative languages
Result language
angličtina
Original language name
Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products
Original language description
With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a 'domino effect' in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10611 - Plant sciences, botany
Result continuities
Project
<a href="/en/project/EF16_026%2F0008413" target="_blank" >EF16_026/0008413: Strategic Partnership for Environmental Technologies and Energy Production</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biotechnology Advances
ISSN
0734-9750
e-ISSN
—
Volume of the periodical
44
Issue of the periodical within the volume
NOV 15
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
107631
UT code for WoS article
000579386600001
EID of the result in the Scopus database
2-s2.0-85091023396