A Brief Review on Aquaculture Genetics, Machine Learning, and Their Convergence

다국어 초록 (Multilingual Abstract)

This concise account on aquaculture, aquaculture genetics, and emerging trends of its convergence with machine learning, a sub-class of artificial intelligence provides, succinct overviews for each of the disciplines separately, their basics, and machine learning approaches in aquaculture genetics, in a consolidative manner to brief their status, applications and prospects.

참고문헌 (Reference)

1 Glasauer SM, "Whole-genome duplication in teleost fishes and its evolutionary consequences" 289 : 1045-1060, 2014

2 Julie K, "What is aquaculture? A brief history of Fish Farming"

3 Bucher P, "Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences" 212 : 563-578, 1990

4 Lackey, R.T, "Water encyclopedia: surface and agricultural water" 121-129, 2005

5 Shalev-Shwartz S, "Understanding machine learning: From theory to algorithms" Cambridge university press 2014

6 Wargelius A, "Transgenic research" Springer International Publishing 101-105, 2019

7 Aristodemou L, "The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data" 55 : 37-51, 2018

8 Wilkins NP, "The rationale and relevance of genetics in aquaculture: an overview" 2 : 209-228, 1981

9 AO (Food and Agriculture Organization of the United Nations), "The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals"

10 Okoli AS, "Sustainable use of CRISPR/Cas in fish aquaculture: the biosafety perspective" 1-21, 2021

11 Fruehe L, "Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes" 30 : 2988-3006, 2021

12 De Verdal H, "Quantifying the genetic parameters of feed efficiency in juvenile Nile tilapia Oreochromis niloticus" 19 : 1-10, 2018

13 Palaiokostas C, "Predicting for disease resistance in aquaculture species using machine learning models" 20 : 100660-, 2021

14 Lutz CG, "Practical genetics for aquaculture" John Wiley & Sons 2008

15 Changadeya, W, "Potential of genetics for aquaculture development in Africa" 2003

16 Vafaie H, "November. Genetic Algorithms as a Tool for Feature Selection in Machine Learning" 200-203, 1992

17 Ongsulee P., "November. Artificial intelligence, machine learning and deep learning" 2017

18 Sadaiappan B, "Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes" 11 : 1-17, 2021

19 Libbrecht MW, "Machine learning applications in genetics and genomics" 161 : 321-332, 2015

20 Javapoint, "Machine learning Life cycle"

21 Javapoint, "Machine Learning Tutorial"

22 Sundaram A, "Issues with RNA-seq analysis in non-model organisms: a salmonid example" 75 : 38-47, 2017

23 Seo D, "Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs" 11 : 241-, 2021

24 Luo Z, "Genomic selection using a subset of SNPs identified by genome-wide association analysis for disease resistance traits in aquaculture species" 539 : 736620-, 2021

25 Zenger KR, "Genomic selection in aquaculture: application, limitations and opportunities with special reference to marine shrimp and pearl oysters" 9 : 693-, 2019

26 Lin Z, "Genomic selection for heterobothriosis resistance concurrent with body size in the tiger pufferfish, Takifugu rubripes" 10 : 1-13, 2020

27 Jacobs A, "Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach" 8 : 1-9, 2018

28 Evans O, "GM salmon farmer recieves exemption for gene-edited tilapia in Argentina"

29 Garcia SM, "Food security and marine capture fisheries: characteristics, trends, drivers and future perspectives" 365 : 2869-2880, 2010

30 Dunham, R. A, "February. Review of the status of aquaculture genetics" 137-166, 2000

31 Singh, DEEPAK, "February. A look into the artificial intelligence and its application in various fields of life" 2018

32 Degroeve S, "Feature subset selection for splice site prediction" 18 : 75-83, 2002

33 Sweet JB, "Draft study on risk assessment: application of annex 1 of decision CP 9/13 to living modified fish. Report for the secretariat of the convention on biological diversity"

34 Heintzman ND, "Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome" 39 : 311-318, 2007

35 Gautam A, "Development of antimicrobial peptide prediction tool for aquaculture industries" 8 : 141-149, 2016

36 Picardi E, "Data mining techniques for the life sciences" 269-284, 2010

37 Bargelloni L, "Data imputation and machine learning improve association analysis and genomic prediction for resistance to fish photobacteriosis in the gilthead sea bream" 20 : 100661-, 2021

38 Shen Y, "Current status of research on aquaculture genetics and genomics-information from ISGA 2018" 4 : 43-47, 2019

39 Ohler U, "Computational analysis of core promoters in the Drosophila genome" 3 : 1-12, 2002

40 Mitchell M, "Complex" 31-39, 1995

41 Ray S, "Commonly used Machine Learning Algorithms. (with Python and R Codes)"

42 Kok JN, "Artificial intelligence: definition, trends, techniques, and cases" 1 : 270-299, 2009

43 Pillay TVR, "Aquaculture: principles and practices (No. Ed. 2)" Blackwell publishing 2005

44 Abdelrahman H, "Aquaculture genomics, genetics and breeding in the United States: current status, challenges, and priorities for future research" 18 : 1-23,

45 Altun AA, "April. Neural network based recognition by using genetic algorithm for feature selection of enhanced fingerprints" Springer 467-476, 2007

46 Gupta D, "An overview of methods maintaining diversity in genetic algorithms" 2 : 56-60, 2012

47 Losordo TM, "An analysis of biological, economic, and engineering factors affecting the cost of fish production in recirculating aquaculture systems" 25 : 193-203, 1994

48 Shapiro J, "Advanced Course on Artificial Intelligence" Springer 146-168, 1999

49 Moyo NA, "A review of the factors affecting tilapia aquaculture production in Southern Africa" 736386-, 2021

50 Oliveira LS, "A methodology for feature selection using multiobjective genetic algorithms for handwritten digit string recognition" 17 : 903-929, 2003