Application of Artificial Intelligence in Applied Biology and Health Sciences
DOI:
https://doi.org/10.51983/ajeat-2022.11.1.3305Keywords:
Artificial Intelligence, Protein Architecture, Drug Discovery, Drug Recycling, Drug Safety, Nutrient Value, Weather ForecastingAbstract
In modern world, artificial intelligence will substitute or magnify human competency in Applied Biology and Health Sciences. AI is the cognitive brilliance manifested by machines or software. AI is growing as an outstanding field in information technology as it enriched the mankind in many circumstances. The application of AI across different disciplines promises an alternative sustainable solution for all human problems. AI is developed in last 10 years as a life changer; when it stated solving human problems easily. This includes solving protein architecture, drug discovery and design, drug recycling, drug safety, diagnose patients, to choose a suitable crop, to determine when to fertilize crops, to avoid adulteration of seeds, to diagnose the crop infections, for proper application of pesticides and herbicides, to identify and removal of weeds, to identify natural ripening of fruits, for the detection of food nutrient value, agri-products for the health and weather forecasting.
References
P. Avneet, "Artificial Intelligence and its application in different areas," International Journal of Engineering and Innovative Technology (IJEIT), vol. 4, no. 10, pp. 79-84, 2015.
K. B. Cohen and L. Hunter, "Getting started in text mining," PLoS Comput Biol, vol. 4, 2008.
T. Berners-Lee, J. Hendler, and O. Lassila, "The semantic web," SciAm., vol. 284, pp. 34-43, 2001.
I. Ahmad, M. U. Akhtar, S. Noor, and A. Shahnaz, "Missing link prediction using common neighbor and centrality based parameterized algorithm," Sci Rep, vol. 10, pp. 1-9, 2020.
J. Sukumaran, E. P. Economo, and L. L. Knowles, "Machine learning biogeographic processes from biotic patterns: a new trait-dependent dispersal and diversification model with model choice by simulation-trained discriminant analysis," Syst Biol, vol. 65, pp. 525-545, 2016.
H. Saarenmaa, N. D. Stone, L. J. Folse, J. M. Packard, W. E. Grant, M. E. Makela, and R. N. Coulson, "An artificial intelligence modeling approach to simulating animal/habitat interactions," Ecol Modell, vol. 44, pp. 125-141, 1988.
H. Soha, J. Felicia, S. Xinghua, S. Brian, W. Jin, and R. Jr. Epaminondas, "Artificial Intelligence for Biology," Integrative and Comparative Biology, pp. 1-9, 2021.
T. Zamvova, "Role of artificial intelligence in biotechnology," Medical and Clinical Research Reports, vol. 2, no. 2, pp. 6-10, 2019.
Q. Kaiyang, G. Fei, L. Xiangrong, L. Yuan, and Z. Quan, "Application of Machine Learning in Microbiology," Frontiers in Microbiology, vol. 827, no. 10, pp. 1-10, 2019.
S. Agrebi, and A. Larbi, "Use of artificial intelligence in infectious diseases," Artificial Intelligence in Precision Health, Academic Press, pp. 415-438, 2020.
T. L. Wiemken, and R. R. Kelley, "Machine learning in epidemiology and health outcomes research," Annu Rev Public Health, vol. 41, pp. 21–36, 2020.
A. Murali, A. Bhargava, and E. S. Wright, "IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences," Microbiome, vol. 6, pp. 140, 2018.
A. Fiannaca, L. L. Paglia, M. L. Rosa, G. L. Bosco, G. Renda, R. Rizzo, G. Salvatore, and U. Alfonso, "Deep learning models for bacteria taxonomic classification of metagenomic data," BMC Bioinformatics, vol. 19, pp. 198, 2018.
D. Amgarten, L. P. P. Braga, A. M. da Silva, and J. C. Setubal, "MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins," Front.Genet, vol. 9, pp. 304, 2018.
S. Roux, F. Enault, B. L. Hurwitz, and M. B. Sullivan, "VirSorter: mining viral signal from microbial genomic data," PeerJ, vol. 3, pp. 985, 2015.
J. Ren, N. A. Ahlgren, Y. Y. Lu, J. A. Fuhrman, and F. Z. Sun, "VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data," Microbiome, vol. 5, pp. 69, 2017.
T. H. Davenport, T. Hongsermeier, and K. A. Mc Cord, "Using AI to improve electronic health records," Harvard Business Review, vol. 12, pp. 1-6, 2016.
M. T. Ahmed and A. M Sahar, Journal of Computer Science, vol. 10, no. 8, pp. 1355-1361, 2014.
B. P. Kolla and B. Mahendran, "Research Trends and Opportunities in Machine Learning in Biotech & Health Sciences," IEEE India Info, vol. 13, no. 4, pp. 82-84, 2018.
H. Zhu, "Big data and artificial intelligence modeling for drug discovery," Annu. Rev. Pharmacol. Toxicol, vol. 60, pp. 573-589, 2020.
H. L. Ciallella and H. Zhu, "Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity," Chem. Res. Toxicol, vol. 32, pp. 536-547, 2019.
H. S. Chan, "Advancing drug discovery via artificial intelligence," Trends Pharmacol. Sci, vol. 40, no. 8, pp. 592-604, 2019.
N. Brown, "Computational Methods to Support Drug Design," Silico Medicinal Chemistry: Royal Society of Chemistry, 2015.
J. C. Pereira, "Boosting docking-based virtual screening with deep learning," J. Chem. Inf. Model, vol. 56, pp. 2495-2506, 2016.
E. Coiera, "Guide to medical informatics, the Internet and telemedicine," Chapman & Hall Ltd, 1997.
T. H. Davenport and J. Glaser, "Just-in-time delivery comes to knowledge management," Harvard Business Review, 2002.
S. Sotirakos, B. Fouda, R. Mohamed, A. Noor, N. Cribben, C. Mulhall, A. O. Byrne, M. Aisling, C. Bridget, and Ruairi, "Harnessing artificial intelligence in cardiac rehabilitation, a systematic review," Future Cardiology, vol. 18, no. 2, pp. 154-164, 2022.
R. Patcas, D.A. Bernini, A. Volokitin, E. Agustsson, R. Rothe, and R.Timofte, "Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age," International Journal of Oral and Maxillofacial Surgery, vol. 48, no. 1, pp. 77-83, 2019.
R. Patcas, R. Timofte, A. Volokitin, E. Agustsson, T. Eliades, M.Eichenberger, and M.M. Bornstein, "Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups," European Journal of Orthodontics, vol. 41, no. 4, pp. 428-433, 2019.
J. S. Cao, Z. Y. Lu, M. Y. Chen, B. Zhang, S. Juengpanich, J. H. Hu, S. J. Li, W. Topatana, X. Y. Zhou, X. Feng, and J. L. Shen, "Artificial intelligence in gastroenterology and hepatology: Status and challenges," World Journal of Gastroenterology, vol. 27, no. 16, pp. 1664-1690, 2021.
N. K. Tran, S. Albahra, L. May, S. Waldman, S. Crabtree, S.Bainbridge and H. Rashidi, "Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing," Clinical Chemistry, vol. 68, no. 1, pp. 125-133, 2021.
N. Kobie, "Deep Mind's new AI can spot breast cancer just as well as your doctor," Wired UK, 2020.
S. M. McKinney, M. Sieniek, V. Godbole, J. Godwin, N. Antropova, H. Ashrafian, T. Back, M. Chesus, G. S. Corrado, A. Darzi, M.Etemadi, F. Garcia-Vicente, F. J. Gilbert, M. Halling-Brown, D.Hassabis, S. Jansen, A. Karthikesalingam, C.J. Kelly, D. King, J. R. Ledsam, D. Melnick, H. Mostofi, L. Peng, J. J. Reicher, B. Romera-Paredes, R. Sidebottom, M. Suleyman, D. Tse, K. C. Young, J. DeFauw and S. Shetty, "International evaluation of an AI system for breast cancer screening," Nature, vol. 577, no. 7788, pp. 89-94, 2020.
"Artificial intelligence identifies prostate cancer with near-perfect accuracy," Eurek Alert, 2020.
L. Pantanowitz, G.M. Quiroga-Garza, L. Bien, R. Heled, D.Laifenfeld, C. Linhart, J. Sandbank, A. Albrecht Shach, V. Shalev, M.Vecsler, P. Michelow, S. Hazelhurst and R. Dhir, "An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study," Lancet Digit Health, vol. 2, no. 8, 2020.
S. Forsch, F. Klauschen, P. Hufnagl, and W. Roth, "Artificial Intelligence in Pathology," Deutsches Arzteblatt International, vol. 118, no. 12, pp. 199–204, 2021.
P. Jurmeister, M. Bockmayr, P. Seegerer, T. Bockmayr, D. Treue, G. Montavon, C. Vollbrecht, A. Arnold, D. Teichmann, K. Bressem, U. Schuller, M.V. Laffert, K.R. Muller, D. Capper and F. Klauschen, "Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases," Sci Transl Med, vol. 11, no. 509, 2019.
A. N. Pisarchik, V. A. Maksimenko and A. E. Hramov, "From Novel Technology to Novel Applications: Comment on “An Integrated Brain-Machine Interface Platform with Thousands of Channels” by Elon Musk and Neuralink," Journal of Medical Internet Research, vol. 21, no. 10, 2019.
M. L. Richardson, E. R. Garwood, Y. Lee, M. D. Li, H. S. Lo, A. Nagaraju, X. V. Nguyen, L. Probyn, P. Rajiah, J. Sin, A. P. Wasnik, and K. Xu, "Noninterpretive Uses of Artificial Intelligence in Radiology," Acad Radiol, vol. 28, no. 9, pp. 1225-1235, 2021.
S. Robert, W. Manuel, K. Emre, R. Christoph and K. David, "Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives," Seminars in Nuclear Medicine, vol. 51, no. 2, pp. 170-177, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 The Research Publication
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.