AI in Bioinformatics
Keywords:intelligent bioinformatics system, AI tools in bioinformatics
In bioinformatics science and computational molecular biology, artificial intelligence (AI) has rapidly gained interest. With the availability of numerous types of AI algorithms, it has become prevalent for researchers to use off-shelf programmes to identify their datasets and mine them. At present, researchers are facing difficulties in selecting the right approach that could be extended to a given data collection, with numerous intelligent approaches available in the literature. Researchers need instruments that present the data in an intuitive manner, annotated with meaning, precision estimates, and description. In the fields of bioinformatics and computational molecular biology (DNA sequencing), this article seeks to review the use of AI. These fields have evolved from the needs of biologists to use the large volumes of data continuously obtained in genomic science and to better understand them. For several approaches to bioinformatics and DNA sequencing, the fundamental impetus is the evolution of species and the difficulty of dealing with incorrect results. The type of software programmes developed by the scientific community to search, identify and mine numerous usable biological databases are also mentioned in this article, simulating biological experiments with and without mistakes. The review of antibody-antigen interactions and their diversity, and the study of epidemiological evidence that can help forecast antibody-antigen interactions and the induction of broadly neutralising antibodies are important questions to be answered in the field of vaccinology.
. S.A. Sehgal, A.H. Mirza, R.A. Tahir, and A. Mir (2018). “Quick Guideline for Computational Drug Design 2018.” Bentham Science Publishers.
. S.A. Sehgal, N.A. Khattak, and A. Mir (2013). “Structural, phylogenetic and docking studies of D-amino acid oxidase activator (DAOA), a candidate schizophrenia gene.” Theoretical Biology and Medical Modelling, 2013, vol. 10(1), pp.3.
. S.A. Sehgal, S. Mannan, S. Kanwal, I. Naveed, and A. Mir (2015). “Adaptive evolution and elucidating the potential inhibitor against schizophrenia to target DAOA (G72) isoforms.” Drug design, development and therapy, vol. 9, pp. 3471.
. P. K. Donepudi (2018). “AI and Machine learning in pharmacy: Systematic review of related literature.” ABC journal of advanced research, Vol.7 (2), pp. 109-112.
. S. A. Sehgal, M. Hassan, and S. Rashid (2014). “Pharmacoinformatics elucidation of potential drug targets against migraine to target ion channel protein KCNK18.” Drug design, development and therapy, Vol. 8, pp. 571.
. P. K. Donepudi (2015). “Crossing point of artificial intelligence in cybersecurity”. American journal of trade and policy, Vol. 2 (3), 121-128. https://doi.org/10.18034/ajtp.v2i3.493
. H. Golkarieh (2019). “How AI is shaping the future of Bioinformatics.” Retrieved November 14, 2020, from https://medium.com/optima-ai/how-ai-is-shaping-the-future-of-bioinformatics-f4aa17bce5a6
. R. Agrawal, and R. Srikant (1994). “Fast algorithms for mining association rules.” BMC Bioinformatics, Vol. 3(35), pp. 12-16.
. D. Bhandari, C.A. Murthy, and S.K. Pal (2012). “Variance as a stopping criterion for genetic algorithms with elitist model.” Fundamata Informaticae, Vol. 120, pp. 145-164.
. P. K. Donepudi (2016). “Influence of cloud computing in business: are they robust?” Asian journal of applied science and engineering, Vol 5(3), pp. 193-196. DOI: https://doi.org/10.5281/zenodo.4110308
. C. Burge, and S. Karlin (1997). “Prediction of complete gene structures in human genomic DNA.” Journal of Molecular Biology, Vol. 268, pp. 78-94.
. H. Douzono, S. Hara, and Y. Noguchi Y (1998). “An application of genetic algorithm to DNA sequencing by oligonucleotide hybridization.” Proceedings of the IEEE international joint symposia on intelligence and systems Rockville, Maryland, USA. 5(34), pp. 92–98
. F. Corpet (1988). “Multiple sequence alignment with hierarchical clustering.” Nucleic Acids Research, Vol. 16 (22), pp. 10881- 10890
. Y.C. Chung, and L.H. Randy (2000). “Amplitude and phase adaptive nulling with a genetic algorithm.” Journal of Electromagnetic Waves and Applications, Vol. 14, (5), pp. 631-649.
. N. Cannata, M. Schröder, R. Marangoni, and P.A. Romano (1992). “Semantic Web for bioinformatics: goals, tools, systems, applications.” BMC Bioinformatics, Vol. 9(4), pp.1.
. L. Hunter, “Artificial intelligence and molecular biology.” San Jose (CA), AAAI Press.
. G. Valentini, R. Tagliaferri, and F. Masulli (2009). “Computational intelligence and machine learning in bioinformatics.” Artif. Intell. Med., Vol. 45, pp. 91–96.
. J. Pitrat (1996). “Artificial intelligence and heuristic methods.” Revue Francaise De Recherche Operationnele, Vol. 10, pp. 137–137.
. S. Kumar, T.W. Banks, and S. Cloutier (2012). “SNP discovery through next-generation sequencing and its applications.” Int J Plant Genom, [cited 2017 Feb 10];2012:831460. DOI:10.1155/2012/831460
. D. Hilbert, J.V. Neumann, and L. Nordheim (1928). “Über die grundlagen der quantenmechanik.” On the fundamentals of quantum mechanics], Math Ann. Vol. 98, pp.1
. M. Al-Haggar, B. Khair-Allaha, and M. Islam (2013). “Bioinformatics in high throughput sequencing: application in evolving genetic diseases.” Jour. Data Mining Genomics Proteomics, vol. 4, 131. DOI: 10.4172/2153-0602.1000131.
. Z. Ezziane (2020). “Applications of artificial intelligence in bioinformatics: A review.” Retrieved from https://www.sciencedirect.com/science/article/pii/S0957417405002344
. G. Piatetsky-Shapiro, and W. Frawley (1991). “Knowledge discovery in databases.” San Jose (CA), AAAI/MIT Press.
. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth (1996). “From data mining to knowledge discovery in databases.” AI Mag, 1996, Vol. 17, 37–54.
. J. Han (2002). “How can data mining help bio-data analysis?” Paper presented at: BIOKDD02: Workshop on Data Mining in Bioinformatics (with SIGKDD02 Conference), Edmonton (Canada). Available from: https://web.njit.edu/∼wangj/publications/biokdd02/01-han.pdf
. N. Esfandiari, M.R. Babavalian, and A.M.E. Moghadam (2014). “Knowledge discovery in medicine: current issue and future trend.” Expert Syst Appl., Vol. 41, pp. 4434–4463.
. N. Padhy, P. Mishra, and R. Panigrahi (2012). “The survey of data mining applications and feature scope.” International Journal Comp Sci Eng Inf Tech. [cited 2017 Feb 10], Vol. 2, 2. DOI:10.5121/ijcseit.2012.2303.
. G. Piatetsky-Shapiro (2017). “CRISP-DM, still the top methodology for analytics, data mining, or data science projects [Internet].” Available from: http://www.kdnuggets.com/2014/10/ crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html
. O. Niakšu (2015). “Development and application of data mining methods in medical diagnostics and healthcare management.” Dissertation, Vilnius: Vilnius University.
. S.A. Sehgal (2017). “Pharmacoinformatics and molecular docking studies reveal potential novel Proline Dehydrogenase (PRODH) compounds for Schizophrenia inhibition.” Medicinal Chemistry Research, 2017, Vol. 26(2), pp. 314-326
. K. Moore, and H. Passley (2020). “Leveraging the Benefits of Artificial Intelligence Technology in Bioinformatics.” Retrieved from https://www.bbntimes.com/technology/ leveraging-the- benefits-of-artificial-intelligence-technology-in-bioinformatics
. G.B. Fogel (2008). “Computational intelligence approaches for pattern discovery in biological systems.” Briefings in Bioinformatics, 2008, Vol. 9 (4), pp. 307–316.
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