Skip to main content
SpringerLink
Log in
Book cover

Emerging Technologies in Biomedical Engineering and Sustainable TeleMedicine pp 13–30Cite as

Artificial Intelligent and Machine Learning Methods in Bioinformatics and Medical Informatics

Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Recently, the machine learning techniques have been widely adopted in the field of bioinformatics and medical informatics. Generally, the main purpose of machine learning is to develop algorithms that can learn and improve over time and can be utilized for predictions in hindcast and forecast applications. Computational intelligence has been significantly employed to develop optimization and prediction solutions for several bioinformatics and medical informatics techniques in which it utilized various computational methodologies to address complex real-world problems and promises to enable computers to help humans in analyzing large complex data sets. Its approaches have been widely applied in biomedical fields, and there are many applications that use the machine learning, such as genomics, proteomics, systems biology, evolution and text mining, which are also discussed. In this chapter, we provide a comprehensive study of the use of artificial intelligent and machine learning methods in bioinformatics and medical informatics, including AI and its learning processes, machine learning and its applications for health informatics, text mining methods, and many other related topics.

Keywords

  • Artificial intelligence
  • Machine learning
  • Bioinformatics
  • Medical informatics
  • Microarray processing
  • Systems biology

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Abbreviations

AI:

Artificial intelligence.

IT:

Information technology.

ML:

Machine learning.

NN:

Neural networks.

EHR:

Electronic health records.

ANI:

Artificial narrow intelligence.

AGI:

Artificial general intelligence.

NLP:

Natural language processing.

SR:

Speech recognition.

ES:

Expert systems.

AI-R:

Artificial intelligence for robotics.

PML:

Probabilistic machine learning.

MLD:

Machine learning data.

aML:

automated Machine Learning.

iML:

interactive Machine learning.

HCI:

Human–computer interaction.

KDD:

Knowledge discovery/data.

RNA:

Ribonucleic acid.

DNA:

Microarray deoxyribonucleic acid.

cDNA:

complementary Microarray deoxyribonucleic acid.

mRNA:

messenger ribonucleic acid.

MIAME:

Minimum information about a microarray experiment.

FGED:

Functional Genomics Data Society.

FRG/BKG:

Foreground/background.

MS:

Mass spectrometry.

SVM:

Support vector machine.

RBF:

Radial basis function.

CART:

Classification and regression tree.

OOB:

Out of the bag.

TM:

Text mining.

IR:

Information retrieval.

DC:

Document classification.

NER/NEN:

Named entity recognition/normalization.

ART:

Adaptive resonance theory.

DNN:

Deep neural network.

References

  1. Brunette E.S, Flemmer RC, Flemmer CL, (2009). A review of artificial intelligence. Proceedings of 4th International Conference on Autonomous Robots and Agents (ICARA 2009), pp: 385–392.

    Google Scholar 

  2. Boden M.A (1998). Creativity and artificial intelligence. Artificial Intelligence 103: 347–356.

    CrossRef  MathSciNet  Google Scholar 

  3. Müller V.C, Bostrom N. (2014). Future progress in artificial intelligence. AI Matters 1: 9–11.

    CrossRef  Google Scholar 

  4. Research Report. (2018). The AI Industry Series: Top Healthcare AI Trends to Watch. Retrieved online: https://www.cbinsights.com/research/report/.

  5. IBM Watson Health (2018). Artificial Intelligence in medicine. Technical Report. Retrieved online: https://www.ibm.com/watson-health/learn/.

  6. Sumit D. et. al. (2015): Applications of Artificial Intelligence in Machine Learning: Review and Prospect. International Journal of Computer Applications, Vol. 115, No. 9, April 2015.

    Google Scholar 

  7. Rahul C. Deo. (2018). Machine Learning in Medicine. Circulation. 2015 Nov 17; 132(20): 1920–1930. https://doi.org/10.1161/circulationaha.115.001593.

  8. Yuedong Y. (2016). Sixty-five years of the long march in protein secondary structure prediction: the final stretch. Briefings in Bioinformatics, Volume 19, Issue 3, 1 May 2018, Pages 482–494, https://doi.org/10.1093/bib/bbw129.

  9. Pedro. L. (2006). Machine learning in bioinformatics. Briefings in Bioinformatics, Volume 7, Issue 1, 1 March 2006, Pages 86–112, https://doi.org/10.1093/bib/bbk007.

  10. Stuart R., Peter N. (2009). Artificial Intelligence: A Modern Approach. Pearson; 3 edition (December 11, 2009).

    Google Scholar 

  11. Linda S. G. (1997). Mainstream Science on Intelligence: An Editorial With 52 Signatories. Ablex Publishing Corporation.

    Google Scholar 

  12. Nick B. (2006). How long before superintelligence? Linguistic and Philosophical Investigations, 2006. - pp. 11–30.

    Google Scholar 

  13. Padraig C, Matthieu C., Sarah J.D Delany. (2008). Supervised Learning: Machine Learning techniques for Multimedia. Springer, Case studies on Organizational and retrieval.

    Google Scholar 

  14. Ricci F., Rokach L., Shapira B. (2010). Recommender Systems Handbook. Boston, MA: Springer. ISBN 9780387858197.

    Google Scholar 

  15. Jordan MI, Mitchell TM (2015). Machine learning: trends, perspectives, and prospects. Science, Vol. 349 No. 6245, P.p. 255–260.

    Google Scholar 

  16. LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature, Vol. 521, No. 7553, P.p.:436–444.

    Google Scholar 

  17. Bayes T. (1763). An essay towards solving a problem in the doctrine of chances (posthumous communicated by Richard Price). Philosophical Transactions of the Royal Society of London 53 (1763), 370–418.].

    Google Scholar 

  18. Barnard GA, Bayes T (1958). Studies in the history of probability and statistics: IX. Thomas Bayes’s essay towards solving a problem in the doctrine of chances. Biometrika, Volume 45, Issue 3–4, 1 December 1958, Pages 293–295, https://doi.org/10.1093/biomet/45.3-4.293.

  19. Hastie T, Tibshirani R, Friedman J (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd edition. Springer, New York.

    CrossRef  Google Scholar 

  20. Murphy KP (2012). Machine learning: a probabilistic perspective. MIT press, Cambridge.

    MATH  Google Scholar 

  21. Silver D, et. al. (2016). Mastering the game of go with deep neural networks and tree search. Nature, Vol. 529, No. 7587, P.p. 484–489.

    Google Scholar 

  22. Zhong N, et. al. (2007). Web intelligence meets brain informatics. Zhong N, Liu JM, Yao YY, Wu JL, Lu SF, Li KC (eds) Web intelligence meets brain informatics., Lecture Notes in Artificial Intelligence 4845, Springer, Berlin, pp 1–31.

    Google Scholar 

  23. Holzinger A (2014). Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intelligence Inform Bull 15(1):6–14.

    Google Scholar 

  24. Mitchell TM (1997). Machine learning. McGraw Hill, New York.

    MATH  Google Scholar 

  25. Holzinger A, Dehmer M, Jurisica I. (2014). Knowledge discovery and interactive data mining in bioinformatics-state-of-the-art, future challenges and research directions. BMC Bio inform 15(S6):I1.

    Google Scholar 

  26. Spinrad N. (2014). Google car takes the test. Nature, Vol. 514, No.7523, P.p. 528–528.

    Google Scholar 

  27. Holzinger A (2014). Biomedical informatics: discovering knowledge in big data. Springer, New York.

    CrossRef  Google Scholar 

  28. Holzinger A (2013). Human–computer interaction and knowledge discovery (HCI-KDD): what is the benefit of bringing those two fields to work together? Multidisciplinary research and practice for information systems., Springer Lecture Notes in Computer Science LNCS 8127Springer, Heidelberg, pp 319–328.

    Google Scholar 

  29. Mitchell, T. (1997). Machine Learning. McGraw-Hill.

    Google Scholar 

  30. Ohler, W., Liao, C., Niemann, H. & Rubin, G. M. Computational analysis of core promoters in the Drosophila genome. Genome Biol. 3, RESEARCH0087 (2002).

    Google Scholar 

  31. Degroeve, S., Baets, B. D., de Peer, Y. V. & Rouzé, P. Feature subset selection for splice site prediction. Bioinformatics 18, S75–S83 (2002).

    CrossRef  Google Scholar 

  32. Bucher, P. (1990). Weight matrix description of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. J. Mol. Biol. 4, 563–578.

    CrossRef  Google Scholar 

  33. Heintzman, N. et al. (2007). Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nature Genet. 39, 311–318.

    CrossRef  Google Scholar 

  34. BioNinja. Microarrays. Retrieved online: http://ib.bioninja.com.au/higher-level/topic-7-nucleic-acids/72-transcription-and-gene/.

  35. Peter B, Lei L. and Mark B. (2007). DNA Microarray Image Processing. DNA Array Image Anal. Nuts Bolts (Nuts Bolts Ser), Pages 1–77.

    Google Scholar 

  36. Adams R. M, B. Stancampiano, M. McKenna and D. Small. (2002). Case Study: A Virtual Environment for Genomic Data Visualization. IEEE Transactions on Visualization, Boston, MA, USA (published as CD).

    Google Scholar 

  37. Affymetrix Inc., Gene Chip Arrays. Retrieved online: http://www.affymetrix.com/index.affx.

  38. Brazma A., et. al, (2001). Minimum Information About a Microarray Experiment (MIAME)–toward standards for microarray data, Nat. Genet. 29, 365–371, December 2001.

    Google Scholar 

  39. Whitfield CW, Cziko AM, Robinson GE. (2003). Gene expression profiles in the brain predict behavior in individual honey bees. Science. Vol. 302, pages 296–9.

    Google Scholar 

  40. Jain A. N., et. al. (2002). Fully Automated Quantification of Microarray Image Data. Genome Research, Vol. 12, No. 2, Feb 2002, pp. 325–332.

    Google Scholar 

  41. Steinfath M., et. al. (2001). Automated image analysis for array hybridization experiments. Bioinformatics, Vol. 17, pages 634–641.

    CrossRef  Google Scholar 

  42. Russ J. (1999). The Image Processing Handbook: Third Edition. CRC Press with IEEE Press. Published by CRC Press LLC. 1999.

    Google Scholar 

  43. Liew A W-C., H. Yan, and M. Yang. (2003). Robust Adaptive Spot Segmentation of DNA Microarray Images. Pattern Recognition 36, pages 1251–1254.

    CrossRef  Google Scholar 

  44. Axon Instruments Inc., GenePix Pro. Retrieved Online at: http://www.axon.com/GN_Genomics.html.

  45. Dodd L. E., et. al., (2004). Correcting Log Ratios for Signal Saturation in cDNA Microarrays. Bioinformatics, Vol. 20, No. 16, pp. 2685–2693.

    CrossRef  Google Scholar 

  46. Kamberova G., S. Shah (editors) (2002). DNA Array Image Analysis - Nuts and Bolts. Data Analysis Tools for DNA Microarrays, DNA Press LLC, MA, 2002.

    Google Scholar 

  47. P. Mallick, et. al. (2007). Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotech, 25(I): I 25-I 31.

    Google Scholar 

  48. W. H. Zhu, J. W Smith and C. M. Huang (2010). Mass Spectrometry-Based LabelFree Quantitative Proteomics. Journal of Biomedicine and Biotechnology, vol. 2010, article ID: 840518.

    Google Scholar 

  49. S. E. Ong and M. Mann (2005). Mass spectrometry-based proteomics turns quantitative. Nature Chemical Biology, vol. 1, pp. 252–262.

    CrossRef  Google Scholar 

  50. R. Aebersold and M. Mann. (2003). Mass spectrometry-based proteomics. Nature, 422, pp. 198–207.

    CrossRef  Google Scholar 

  51. L. N. Mueller, M. Y. Brusniak, D. R. Mani and R. Aebersold (2008). An Assessment of Software Solutions for the Analysis of Mass Spectrometry Based Quantitative Proteomics Data. J. Proteome Res., 7 (01), pp. 51–61.

    CrossRef  Google Scholar 

  52. P. Lu, C. Vogel, R. Wang, X. Yao and E. M. Marcotte. (2007). Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nature Biotechnology, 25, pp. 117–124.

    CrossRef  Google Scholar 

  53. J. C. Braisted, et. al., (2008) The Apex Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results, BMC Bioinformatics 9:529.

    CrossRef  Google Scholar 

  54. S. Kawashima and M. Kanehisa. (2000). AA_index: amino acid index database. Nucleic Acids Res.vol. 28, no. 374.

    Google Scholar 

  55. Biao H., Baochang Z., Yan. F., (2013). Discovery of Proteomics based on Machine Learning. Quantitative Biology > Quantitative Methods.

    Google Scholar 

  56. B. M. Webb-Robertson, et. al., (2008). A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics, Bioinformatics Corrigendum, vol.26, no.13, pp. 1677–1683.

    CrossRef  Google Scholar 

  57. C. Cortes and V. Vladimir. (1995). Support-Vector Networks, Machine Learning, Vol. 20, Issue. 3, pp. 273–297.

    MATH  Google Scholar 

  58. E. Alpaydin (2004). Introduction to Machine Learning, MIT Press.

    Google Scholar 

  59. L. Brieman (2001). Random Forest, Machine Learning, vol.45 issue.1, pp. 5–32.

    CrossRef  Google Scholar 

  60. Fie. Z., et. al, (2013). Biomedical text mining and its applications in cancer research. Journal of Biomedical Informatics 46 (2013) 200–211.

    Google Scholar 

  61. Frawley W.J, Piatetsky S.G, Matheus C. J. (1992). Knowledge discovery in databases: an overview. AI Mag 1992;13:57–70.

    Google Scholar 

  62. Agarwal S, Liu F, Yu H. (2011). Simple and efficient machine learning frameworks for identifying protein–protein interaction relevant articles and experimental methods used to study the interactions. BMC Bioinformatics 2011;12 (Suppl.8):S10.

    Google Scholar 

  63. Rosenblatt F (1958) The perceptron: A probabilistic model for information storage and organization in the brain. Psychol Rev 65: 386–408.

    CrossRef  Google Scholar 

  64. Carpenter GA, Grossberg S (1988). The art of adaptive pattern recognition by a self-organizing neural network. Computer 21: 77–88.

    CrossRef  Google Scholar 

  65. Fukushima K (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36: 193–202.

    CrossRef  Google Scholar 

  66. Weston J, et al. (2005) Semi-supervised protein classification using cluster kernels. Bioinformatics 21: 3241–3247.

    CrossRef  Google Scholar 

  67. Alizadeh A.A, et al. (2000). Distinct type of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, Vol. 403, pages 503–510.

    CrossRef  Google Scholar 

  68. Perou CM, et. al. (1999). Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A 96: 9212–9217.

    CrossRef  Google Scholar 

  69. Alon U, et al. (1999). Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by nucleotide arrays. Proc Natl Acad Sci U S A 96: 6745–6750.

    CrossRef  Google Scholar 

  70. Ross DT, et al. (2000). Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 24: 227–235.

    CrossRef  Google Scholar 

  71. Rost B, Sander C (1994) Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 19: 55–72.

    CrossRef  Google Scholar 

  72. Tarca AL, Cooke JE, Mackay J (2005) A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data. Bioinformatics 21: 2674–2683.

    CrossRef  Google Scholar 

  73. Christof A., et. al., (2016). Deep learning for computational biology. Published online 29.07.2016 Molecular Systems Biology (2016) 12, 878, https://doi.org/10.15252/msb.20156651.

  74. Tin-Chih T.C, Cheng L. L., Hong D. L., (2018). Advanced Artificial Neural Networks. MDPI, Algorithms 2018, 11, 102; https://doi.org/10.3390/a11070102.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, King Faisal University, Hufof, Al-Ahsa, Saudi Arabia

    Noor A. Jebril,

  2. Department of Data Science & Artificial Intelligence, University of Petra (UoP),, Amman, Jordan

    Qasem Abu Al-Haija,

Authors
  1. Noor A. Jebril,
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qasem Abu Al-Haija,
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qasem Abu Al-Haija, .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar

    Prof. Dr. Jihad Alja’am

  2. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar

    Dr. Somaya Al-Maadeed

  3. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar

    Dr. Osama Halabi

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jebril,, N.A., Abu Al-Haija,, Q. (2021). Artificial Intelligent and Machine Learning Methods in Bioinformatics and Medical Informatics. In: Alja’am, J., Al-Maadeed, S., Halabi, O. (eds) Emerging Technologies in Biomedical Engineering and Sustainable TeleMedicine. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-14647-4_2

Download citation

search

Navigation