Abstract
Bioinformatics is a multidisciplinary field of managing health information digitally, which can be used further for analyzing and justifying the biological behavior of the nature. In the advancement of different computational tools and algorithms, these biological data can be managed very efficiently, and by analyzing those data, it is very much possible to find and discover different unknown mysteries of nature like the cause of happening any disease, the evolution of biological objects like virus, bacteria or any kind of living species, customized drug management, prediction of protein structure, DNA sequencing, etc. In the era of digitization, there are huge amount of biological data that are now possible to store in digital platform, and these can be processed through various computational tools that can analyze those data and produce various statistical reports. Machine learning in the domain of artificial intelligence is now a common tool for different bioinformatics applications. The main advantages of machine learning techniques are to predict the optimized results based on the previous data record. In this paper, we will discuss about the different tools and its application on bioinformatics and the artificial intelligence approach to the bioinformatics application.
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References
Mantas J (2016) Biomedical and health informatics education—the IMIA Years. Yearb Med Inform (Suppl. 1):S92–S102
Thampi SM (2009) Introduction to bioinformatics. arXiv:0911.4230
Can T (2014) Introduction to bioinformatics. In: Yousef M, Allmer J (eds) miRNomics: microRNA biology and computational analysis. Humana Press, Totowa, NJ, pp 51–71
Sree Divya K, Bhargavi P, Singaraju J (2018) Machine learning algorithms in big data analytics. Int J Comput Sci Eng 6:63–70
Kumar S, Singh M (2019) Big data analytics for healthcare industry: impact, applications, and tools. Big Data Min Anal 2(1):48–57
O’Driscoll A, Daugelaite J, Sleator RD (2013) ‘Big data’, Hadoop and cloud computing in genomics. J Biomed Inform 46(5):774–781
Nicolas J (27 July 2018) Artificial intelligence and bioinformatics. Available from https://hal.inria.fr/hal-01850570
Hogeweg P (2011) The roots of bioinformatics in theoretical biology. PLoS Comput Biol 7(3):e1002021
Lindsay RK et al (1993) DENDRAL: a case study of the first expert system for scientific hypothesis formation. Artif Intell 61(2):209–261
Hayes-Roth B et al (1986) PROTEAN: deriving protein structure from constraints. American Association for Artificial Intelligence
Feigenbaum EA, Buchanan BG (1993) DENDRAL and Meta-DENDRAL: roots of knowledge systems and expert system applications. Artif Intell 59(1):233–240
Müller UR, Nicolau DV (2005) Microarray technology and its applications. Springer, Berlin
Narayanan A, Keedwell E, Olsson B (2002) Artificial intelligence techniques for bioinformatics. App Bioinform 1:191–222
Oyelade O et al (2015) Bioinformatics, healthcare informatics and analytics: an imperative for improved healthcare system. Int J Appl Inf Syst 8(5):1–6
Bagga PS (2012) Development of an undergraduate bioinformatics degree program at a liberal arts college. Yale J Biol Med 85(3):309–321
Koonin EV, Galperin M (2013) Sequence—evolution—function: computational approaches in comparative genomics. Springer Science & Business Media, New York
Zvelebil MJ, Baum JO (2007) Understanding bioinformatics. Garland Science, New York
Krogh A (1998) An introduction to hidden Markov models for biological sequences. In: Salzberg SL, Searls DB, Kasif S (eds) New comprehensive biochemistry, Chapter 4. Elsevier, Amsterdam, pp 45–63
Ji S (2004) Molecular information theory: solving the mysteries of DNA. In: Ciobanu G, Rozenberg G (eds) Modelling in molecular biology. Springer, Berlin, Heidelberg, pp 141–150
Ezziane Z (2006) Applications of artificial intelligence in bioinformatics: a review. Expert Syst Appl 30(1):2–10
Breda A et al (2007) Protein structure, modelling and applications. National Center for Biotechnology Information (US)
Gopal K et al (2006) TEXTAL: crystallographic protein model building using AI and pattern recognition. AI Mag 27(3):15
Holbrook SR, Muskal SM, Kim S-H (1993) Predicting protein structural features with artificial neural networks. In: Artificial intelligence and molecular biology. AAAI Press, Menlo Park, p 162
Kellis M et al (2014) Defining functional DNA elements in the human genome. Proc Natl Acad Sci USA 111(17):6131–6138
Yip KY, Cheng C, Gerstein M (2013) Machine learning and genome annotation: a match meant to be? Genome Biol 14(5):205
Eddy SR (1998) Profile hidden Markov models. Bioinform (Oxford, England) 14(9):755–763
Pellegrini M et al (1999) Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci USA 96(8):4285–4288
Mattfeldt T et al (2001) Cluster analysis of comparative genomic hybridization (CGH) data using self-organizing maps: application to prostate carcinomas. Anal Cell Pathol: J Eur Soc Anal Cell Pathol 23(1):29–37
Gill SK et al (2016) Emerging role of bioinformatics tools and software in evolution of clinical research. Perspect Clin Res 7(3):115–122
Fujiwara T, Kamada M, Okuno Y (2018) Artificial intelligence in drug discovery. Gan To Kagaku Ryoho 45(4):593–596
Duch W, Swaminathan K, Meller J (2007) Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des 13(14):1497–1508
Agrawal P (2018) Artificial intelligence in drug discovery and development. J Pharmacovigil 6:2
Food and Drug Administration (2019) Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based Software as a Medical Device (SaMD). Discussion paper and request for feedback
Altschul SF et al (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410
Krogh A et al (1994) Hidden Markov models in computational biology: applications to protein modeling. J Mol Biol 235(5):1501–1531
Daugelaite J, Driscoll AO’, Sleator RD (2013) An overview of multiple sequence alignments and cloud computing in bioinformatics. ISRN Biomath. 2013:14
American Association for the Advancement of Science (2005) Tools: a bigger blast. Sci 309(5743):1971
Garg VK et al (2016) MFPPI—Multi FASTA ProtParam Interface. Bioinformation 12(2):74–77
Allen JE et al (2006) JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions. Genome Biol 7(Suppl 1):S9.1–S9.13
Rombel IT et al (2002) ORF-FINDER: a vector for high-throughput gene identification. Gene 282(1–2):33–41
Kanhere A, Bansal M (2005) A novel method for prokaryotic promoter prediction based on DNA stability. BMC Bioinform 6:1
Munch R et al (2005) Virtual footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes. Bioinform 21(22):4187–4189
Mitra A et al (2011) WebGeSTer DB—a transcription terminator database. Nucleic Acids Res. 39(Database issue):D129–D135
Burge C, Karlin S (1997) Prediction of complete gene structures in human genomic DNA11. Edited by F. E. Cohen. J Mol Biol 268(1):78–94
Mehmood MA, Sehar U, Ahmad N (2014) Use of bioinformatics tools in different spheres of life sciences. J. Data Min. Genomics & Proteomics 5(2):1
Kidd KK, Sgaramella-Zonta LA (1971) Phylogenetic analysis: concepts and methods. Am J Hum Genet 23(3):235–252
Doroshkov AV et al (2019) The evolution of gene regulatory networks controlling Arabidopsis thaliana L. trichome development. BMC Plant Biol 19(Suppl. 1):53
Kumar S et al (2008) MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. Brief Bioinform 9(4):299–306
Katara P (2013) Role of bioinformatics and pharmacogenomics in drug discovery and development process. Netw Model Anal Health Inform Bioinform 2(4):225–230
Majhi V, Paul S, Jain R (2019) Bioinformatics for healthcare applications. In: 2019 Amity international conference on artificial intelligence (AICAI)
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Majhi, V., Paul, S. (2020). Artificial Intelligence in Bioinformatics. In: Jain, S., Sood, M., Paul, S. (eds) Advances in Computational Intelligence Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2620-6_12
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DOI: https://doi.org/10.1007/978-981-15-2620-6_12
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