Skip to main content
SpringerLink
Log in

Explainable Artificial Intelligence in Genomic Sequence for Healthcare Systems Prediction

  • Chapter
  • First Online:
Connected e-Health

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1021))

Abstract

Various Classification techniques have been developed in past years and applied on genomic sequence for the dynamic modelling. These methods have resulted to impressive answers in term of correctness and analytical capability. Most of their techniques and applications based on Black-box models that use more understandable methodologies that are supported and verified by the scientific world, thus limited the power of interpretations. Despite the development and application of many statistical and machine learning approaches to expose genomic sequence for disease prediction, integrative understanding of the massive statistical and ML remains a challenge. Hence, the introduction and application of Explainable Artificial Intelligence (XAI) paradigm has provides a solution for this problem, were rule-based methods are particularly well suited to explanatory purposes. Additional steps toward more explanatory and genomic sequence sound models include integrating the technique of data gathering with sequence analysis and route studies. Therefore, this chapter present the applicability of XAI in genomic sequence for healthcare system. Also, the chapter discusses the challenges facing using eXplainable AI in genomic sequence for disease prediction and diagnosis, and in the healthcare system generally.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
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

Institutional subscriptions

Similar content being viewed by others

References

  1. Lu C, Tang X (2015) Surpassing human-level face verification performance on LFW with Gaussian Face. In: Proceedings of the AAAI conference on artificial intelligence, vol 29, no 1, Mar 2015

    Google Scholar 

  2. Cireşan D, Meier U, Masci J, Schmidhuber J (2011) A committee of neural networks for traffic sign classification. In: The 2011 international joint conference on neural networks. IEEE, July 2011, pp 1918–1921

    Google Scholar 

  3. Awotunde JB, Jimoh RG, Oladipo ID, Abdulraheem M, Jimoh TB, Ajamu GJ (2021) Big data and data analytics for an enhanced COVID-19 epidemic management. Stud Syst Decis Control 2021(358):11–29

    Article  Google Scholar 

  4. Moravčík M, Schmid M, Burch N, Lisý V, Morrill D, Bard N, Davis T, Waugh K, Johanson M, Bowling M (2017) Deepstack: expert-level artificial intelligence in heads-up no-limit poker. Science 356(6337):508–513

    Google Scholar 

  5. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, Chen Y (2017) Mastering the game of go without human knowledge. Nature 550(7676):354–359

    Google Scholar 

  6. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

    Google Scholar 

  7. Xia H, Liu J, Zhang ZJ (2020) Identifying Fintech risk through machine learning: analyzing the Q&A text of an online loan investment platform. Annals Oper Res 1–21

    Google Scholar 

  8. Awotunde JB, Ogundokun RO, Ayo FE, Matiluko OE (2020) Speech segregation in background noise based on deep learning. IEEE Access 8:169568–169575

    Article  Google Scholar 

  9. Li J, Du T, Ji S, Zhang R, Lu Q, Yang M, Wang T (2020) Textshield: robust text classification based on multimodal embedding and neural machine translation. In: 29th {USENIX} security symposium ({USENIX} Security 20, pp 1381–1398

    Google Scholar 

  10. Ayo FE, Awotunde JB, Ogundokun RO, Folorunso SO, Adekunle AO (2020) A decision support system for multi-target disease diagnosis: a bioinformatics approach. Heliyon 6(3):e03657

    Google Scholar 

  11. Oladipo ID, Babatunde AO, Awotunde JB, Abdulraheem M (2021) An improved hybridization in the diagnosis of diabetes mellitus using selected computational intelligence. Commun Comput Inform Sci 2021(1350):272–285

    Article  Google Scholar 

  12. Awotunde JB, Jimoh RG, Oladipo ID, Abdulraheem M (2021) Prediction of malaria fever using long-short-term memory and big data. Commun Comput Inform Sci 2021(1350):41–53

    Article  Google Scholar 

  13. Awotunde JB, Folorunso OS, Chakraborty C, Bhoi AK, Ajamu GJ (2022) Application of artificial intelligence and big data for fighting COVID-19 pandemic. In: Hassan SA, Mohamed AW, Alnowibet KA (eds) Decision sciences for COVID-19. International series in operations research & management science, vol 320. Springer, Cham

    Google Scholar 

  14. Folorunso SO, Awotunde JB, Adeniyi EA, Abiodun KM, Ayo FE (2021) Heart disease classification using machine learning models. Communications in computer and information science (CCIS), 2022, vol 1547, pp 35–49

    Google Scholar 

  15. Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A (2017) Quantum-chemical insights from deep tensor neural networks. Nat Commun 8(1):1–8

    Article  Google Scholar 

  16. Awotunde JB, Jimoh RG, Abdul Raheem, M, Oladipo ID, Folorunso SO, Ajamu GJ (2022) IoT-based wearable body sensor network for COVID-19 Pandemic. Adv Data Sci Intell Data Commun Technol COVID-19 253–275

    Google Scholar 

  17. Folorunso SO, Awotunde JB, Ayo FE, Abdullah KKA (2021) RADIoT: the unifying framework for iot, radiomics and deep learning modeling. Intell Syst Ref Library 209:109–128

    Article  Google Scholar 

  18. LeCun YA, Bottou L, Orr GB, Müller KR (2012) Efficient BackProp BT-neural networks: tricks of the trade. In: Neural networks: tricks of the trade

    Google Scholar 

  19. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1725–1732

    Google Scholar 

  20. Awotunde JB, Folorunso SO, Jimoh RG, Adeniyi EA, Abiodun KM, Ajamu GJ (2021) Application of artificial intelligence for COVID-19 epidemic: an exploratory study, opportunities, challenges, and future prospects. Stud Syst Decis Control 2021(358):47–61

    Article  Google Scholar 

  21. Awotunde JB, Folorunso SO, Bhoi AK, Adebayo PO, Ijaz MF (2021) Disease diagnosis system for IoT-based wearable body sensors with machine learning algorithm. Intell Syst Ref Library 2021(209):201–222

    Article  Google Scholar 

  22. Samek W, Müller KR (2019) Towards explainable artificial intelligence. In: Explainable AI: interpreting, explaining and visualizing deep learning. Springer, Cham, pp 5–22

    Google Scholar 

  23. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A (2012) NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41(D1):D991–D995

    Google Scholar 

  24. Awotunde JB, Bhoi AK, Barsocchi P (2021) Hybrid cloud/fog environment for healthcare: an exploratory study, opportunities, challenges, and future prospects. Intell Syst Ref Library 2021(209):1–20

    Google Scholar 

  25. Teixeira MC, Monteiro PT, Palma M, Costa C, Godinho CP, Pais P, Cavalheiro M, Antunes M, Lemos A, Pedreira T, Sá-Correia I (2018) YEASTRACT: an upgraded database for the analysis of transcription regulatory networks in Saccharomyces cerevisiae. Nucleic Acids Res 46(D1):D348-D353

    Google Scholar 

  26. Samek W, Wiegand T, Müller KR (2017) Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296

  27. Castelvecchi D (2016) Can we open the black box of AI? Nature News 538(7623):20

    Article  Google Scholar 

  28. Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Mach Intell 1(5):206–215

    Article  Google Scholar 

  29. Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608

  30. Fournier-Viger P, Lin JCW, Kiran RU, Koh YS, Thomas R (2017) A survey of sequential pattern mining. Data Sci Pattern Recognit 1(1):54–77

    Google Scholar 

  31. Alves R, Rodriguez-Baena DS, Aguilar-Ruiz JS (2010) Gene association analysis: a survey of frequent pattern mining from gene expression data. Brief Bioinform 11(2):210–224

    Article  Google Scholar 

  32. Baehrens D, Schroeter T, Harmeling S, Kawanabe M, Hansen K, Müller KR (2010) How to explain individual classification decisions. J Mach Learn Res 11:1803–1831

    MathSciNet  MATH  Google Scholar 

  33. Bach S, Binder A, Montavon G, Klauschen F, Müller KR, Samek W (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7):e0130140

    Google Scholar 

  34. Awotunde JB, Ogundokun RO, Adeniy AE, Abiodun KM, Ajamu GJ (2022) Application of mathematical modelling approach in COVID-19 transmission and interventions strategies. Studies in systems, decision and control, vol 366, pp 283–314

    Google Scholar 

  35. Ribeiro MT, Singh S, Guestrin C (2016) "Why should i trust you?" Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, Aug 2016, pp 1135–1144

    Google Scholar 

  36. Lakkaraju H, Bastani O (2020) “How do I fool you?” Manipulating user trust via misleading black box explanations. In: Proceedings of the AAAI/ACM conference on AI, ethics, and society, Feb 2020, pp 79–85

    Google Scholar 

  37. Güngör O, Güngör T, Uskudarli S (2020) EXSEQREG: explaining sequence-based NLP tasks with regions with a case study using morphological features for named entity recognition. Plos One 15(12):e0244179

    Google Scholar 

  38. Aas K, Jullum M, Løland A (2021) Explaining individual predictions when features are dependent: more accurate approximations to Shapley values. Artif Intell 103502

    Google Scholar 

  39. Landecker W, Thomure MD, Bettencourt LM, Mitchell M, Kenyon GT, Brumby SP (2013) Interpreting individual classifications of hierarchical networks. In: 2013 IEEE symposium on computational intelligence and data mining (CIDM). IEEE, Apr 2013, pp 32–38

    Google Scholar 

  40. Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034

  41. Awotunde JB, Abiodun KM, Adeniyi EA, Folorunso SO, Jimoh RG (2021) A deep learning-based intrusion detection technique for a secured IoMT system. In: International conference on informatics and intelligent applications. Springer, Cham, pp 50–62

    Google Scholar 

  42. Erhan D, Bengio Y, Courville A, Vincent P (2009) Visualizing higher-layer features of a deep network. Univ Montreal 1341(3):1

    Google Scholar 

  43. Locatello F, Bauer S, Lucic M, Raetsch G, Gelly S, Schölkopf B, Bachem O (2019) Challenging common assumptions in the unsupervised learning of disentangled representations. In: international conference on machine learning. PMLR, May 2019, pp 4114–4124

    Google Scholar 

  44. Park JJ, Florence P, Straub J, Newcombe R, Lovegrove S (2019) Deepsdf: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 165–174

    Google Scholar 

  45. Villmann T (2020) Quantum-inspired learning vector quantization basic concepts and beyond. Comput Intell-MiWoCI 2020

    Google Scholar 

  46. Yang G, Ye Q, Xia J (2021) Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond. arXiv preprint arXiv:2102.01998

  47. Došilović FK, Brčić M, Hlupić N (2018) Explainable artificial intelligence: a survey. In: 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, May 2018, pp 0210–0215

    Google Scholar 

  48. Israelsen BW (2017) “I can assure you [...] that it’s going to be all right”–a definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships. Visited on Nov 24 2018

    Google Scholar 

  49. Lipton ZC (2018) The Mythos of Model Interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31–57

    Article  Google Scholar 

  50. Nguyen TT, Hui PM, Harper FM, Terveen L, Konstan JA (2014) Exploring the filter bubble: the effect of using recommender systems on content diversity. In: Proceedings of the 23rd international conference on World wide web, Apr 2014, pp 677–686

    Google Scholar 

  51. Wang L, Han W, Soong FK (2012) High quality lip-sync animation for 3D photo-realistic talking head. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 4529–4532)

    Google Scholar 

  52. Kucharski A (2016) Study epidemiology of fake news. Nature 540(7634):525–525

    Article  Google Scholar 

  53. Biggio B, Corona I, Maiorca D, Nelson B, Šrndić N, Laskov P, Giacinto G, Roli F (2013) Evasion attacks against machine learning at test time. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, Sept 2013, pp 387–402

    Google Scholar 

  54. Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199

  55. Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572

  56. Thys S, Van Ranst W, Goedemé T (2019) Fooling automated surveillance cameras: adversarial patches to attack person detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 0–0

    Google Scholar 

  57. Csiszár O, Csiszár G, Dombi J (2020) Interpretable neural networks based on continuous-valued logic and multicriteria decision operators. Knowl-Based Syst 199:105972

    Google Scholar 

  58. Shickel B, Loftus TJ, Adhikari L, Ozrazgat-Baslanti T, Bihorac A, Rashidi P (2019) DeepSOFA: a continuous acuity score for critically ill patients using clinically interpretable deep learning. Sci Rep 9(1):1–12

    Article  Google Scholar 

  59. Lauritsen SM, Kristensen M, Olsen MV, Larsen MS, Lauritsen KM, Jørgensen MJ, Lange J, Thiesson B (2020) Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature Commun 11(1):1–11

    Google Scholar 

  60. Choi E, Bahadori MT, Kulas JA, Schuetz A, Stewart WF, Sun J (2016) Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. arXiv preprint arXiv:1608.05745

  61. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. NPJ Digital Med 1(1):1–4

    Article  Google Scholar 

  62. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nature Med 25(1):24–29

    Google Scholar 

  63. Rath M, Mishra S (2019) Advanced-level security in network and real-time applications using machine learning approaches. In: Machine learning and cognitive science applications in cyber security. IGI Global, pp 84–104

    Google Scholar 

  64. Shortliffe EH, Sepúlveda MJ (2018) Clinical decision support in the era of artificial intelligence. JAMA 320(21):2199–2200

    Article  Google Scholar 

  65. Humphreys P (2009) The philosophical novelty of computer simulation methods. Synthese 169(3):615–626

    Article  MathSciNet  Google Scholar 

  66. Yan C, Yao J, Li R, Xu Z, Huang J (2018) Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays. In: Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics, pp 103–110

    Google Scholar 

  67. Obermeyer Z, Emanuel EJ (2016) Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med 375(13):1216

    Article  Google Scholar 

  68. Berner ES, La Lande TJ (2007) Overview of clinical decision support systems. In: Clinical decision support systems. Springer, New York, NY, pp 3–22

    Google Scholar 

  69. Mishra S, Tadesse Y, Dash A, Jena L, Ranjan P (2021) Thyroid disorder analysis using random forest classifier. In: Intelligent and cloud computing. Springer, Singapore, pp 385–390

    Google Scholar 

  70. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Google Scholar 

  71. European Group on Ethics in Science and New Technologies to the European Commission (2018) Statement on artificial intelligence, robotics and 'autonomous' systems: Brussels, 9 Mar 2018. Publications Office of the European Union

    Google Scholar 

  72. Marchand K, Foreman J, MacDonald S, Harrison S, Schechter MT, Oviedo-Joekes E (2020) Building healthcare provider relationships for patient-centered care: a qualitative study of the experiences of people receiving injectable opioid agonist treatment. Subst Abuse Treat Prev Policy 15(1):1–9

    Article  Google Scholar 

  73. Durán JM, Jongsma KR (2021) Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI. J Med Ethics 47(5):329–335

    Google Scholar 

  74. Colaner N (2021) Is explainable artificial intelligence intrinsically valuable?. AI & SOCIETY 1–8

    Google Scholar 

  75. Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, García S, Gil-López S, Molina D, Benjamins R, Chatila R, Herrera, F (2020) Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fusion 58:82–115

    Google Scholar 

  76. Gunning D (2017) Explainable artificial intelligence (xai). Defense Adv Res Projects Agency (DARPA), Web 2(2)

    Google Scholar 

  77. Davis M (2012) A plea for judgment. Sci Eng Ethics 18(4):789–808

    Article  MathSciNet  Google Scholar 

  78. McDougall RJ (2019) Computer knows best? The need for value-flexibility in medical AI. J Med Ethics 45(3):156–160

    Article  MathSciNet  Google Scholar 

  79. Hodgkin PK (2016) The computer may be assessing you now, but who decided its values?. Bmj 355

    Google Scholar 

  80. Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56

    Article  Google Scholar 

  81. Goldhahn J, Rampton V, Spinas GA (2018) Could artificial intelligence make doctors obsolete?. Bmj 363

    Google Scholar 

  82. Coiera E (2018) The fate of medicine in the time of AI. Lancet 392(10162):2331–2332

    Article  Google Scholar 

  83. Green ED, Gunter C, Biesecker LG, Di Francesco V, Easter CL, Feingold EA, Felsenfeld AL, Kaufman DJ, Ostrander EA, Pavan WJ, Phillippy AM (2020) Strategic vision for improving human health at The Forefront of Genomics. Nature 586(7831):683-692

    Google Scholar 

  84. Awotunde JB, Ayo FE, Jimoh RG, Ogundokun RO, Matiluko OE, Oladipo ID, Abdulraheem M (2020) Prediction and classification of diabetes mellitus using genomic data. In: Intelligent IoT systems in personalized health care, pp 235–292

    Google Scholar 

  85. Capalbo A, Poli M, Riera-Escamilla A, Shukla V, Kudo Høffding M, Krausz C, Hoffmann ER, Simon C (2021) Preconception genome medicine: current state and future perspectives to improve infertility diagnosis and reproductive and health outcomes based on individual genomic data. Human Reprod Update 27(2):254-279

    Google Scholar 

  86. McGuire AL, Gabriel S, Tishkoff SA, Wonkam A, Chakravarti A, Furlong EE, Treutlein B, Meissner A, Chang HY, López-Bigas N, Segal E (2020) The road ahead in genetics and genomics. Nature Rev Genet 21(10):581-596

    Google Scholar 

  87. Strianese O, Rizzo F, Ciccarelli M, Galasso G, D’Agostino Y, Salvati A, Del Giudice C, Tesorio P, Rusciano MR (2020) Precision and personalized medicine: how genomic approach improves the management of cardiovascular and neurodegenerative disease. Genes 11(7):747

    Google Scholar 

  88. Bravo ML, Santiago-Angelino TM, González-Robledo LM, Nigenda G, Seiglie JA, Serván-Mori E (2020) Incorporating genomic medicine into primary-level health care for chronic non-communicable diseases in Mexico: a qualitative study. Int J Health Plann Manage 35(6):1426–1437

    Article  Google Scholar 

  89. Stark Z, Dolman L, Manolio TA, Ozenberger B, Hill SL, Caulfied MJ, Levy Y, Glazer D, Wilson J, Lawler M, Boughtwood T, North KN (2019) Integrating genomics into healthcare: a global responsibility. Am J Human Genet 104(1):13–20

    Google Scholar 

  90. Gaff CL, Winship IM, Forrest SM, Hansen DP, Clark J, Waring PM, South M, Sinclair AH (2017) Preparing for genomic medicine: a real world demonstration of health system change. NPJ Genomic Med 2(1):1–9

    Google Scholar 

  91. Klein ME, Parvez MM, Shin JG (2017) Clinical implementation of pharmacogenomics for personalized precision medicine: barriers and solutions. J Pharm Sci 106(9):2368–2379

    Article  Google Scholar 

  92. Saha S, Shippy TD, Brown SJ, Benoit JB, D’Elia T (2021) Undergraduate virtual engagement in community genome annotation provides flexibility to overcome course disruptions. J Microbiol biol Educ 22(1)

    Google Scholar 

  93. eMERGE Consortium (2021) Lessons learned from the eMERGE network: balancing genomics in discovery and practice. Human Genet Genomics Adv 2(1):100018

    Google Scholar 

  94. Berry NK (2020) Clinical use of SNP-microarrays for the detection of genome-wide changes in haematological malignancies with a focus on B-cell neoplasms (Doctoral dissertation, The University of Newcastle, Australia)

    Google Scholar 

  95. Marchant G, Barnes M, Evans JP, LeRoy B, Wolf SM (2020) From genetics to genomics: facing the liability implications in clinical care. J Law Med Ethics 48(1):11–43

    Article  Google Scholar 

  96. Lu H, Zhang J, Chen YE, Garcia-Barrio MT (2021) Integration of transformative platforms for the discovery of causative genes in cardiovascular diseases. Cardiovasc Drugs Therapy 1–18

    Google Scholar 

  97. Brazma A, Parkinson H, Schlitt T, Shojatalab M (2001) A quick introduction to elements of biology-cells, molecules, genes, functional genomics, microarrays. EMBL-EBI

    Google Scholar 

  98. Farouq MW, Boulila W, Hussain Z, Rashid A, Shah M, Hussain S, Ng N, Ng D, Hanif H, Shaikh MG, Sheikh A (2021) A novel coupled reaction-diffusion system for explainable gene expression profiling. Sensors 21(6):2190

    Google Scholar 

  99. di Fagagna FDA (2014) A direct role for small non-coding RNAs in DNA damage response. Trends Cell Biol 24(3):171–178

    Article  Google Scholar 

  100. Nair L, Chung H, Basu U (2020) Regulation of long non-coding RNAs and genome dynamics by the RNA surveillance machinery. Nat Rev Mol Cell Biol 21(3):123–136

    Article  Google Scholar 

  101. Phillips T (2008) Small non-coding RNA and gene expression. Nature Educ 1(1):115

    Google Scholar 

  102. Marshall HE, Merchant K, Stamler JS (2000) Nitrosation and oxidation in the regulation of gene expression. FASEB J 14(13):1889–1900

    Article  Google Scholar 

  103. Liu Y (2004) Active learning with support vector machine applied to gene expression data for cancer classification. J Chem Inf Comput Sci 44(6):1936–1941

    Article  Google Scholar 

  104. Glaab E, Bacardit J, Garibaldi JM, Krasnogor N (2012) Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. PloS One 7(7):e39932

    Google Scholar 

  105. Jiang Z, Li T, Min W, Qi Z, Rao Y (2017) Fuzzy c-means clustering based on weights and gene expression programming. Pattern Recogn Lett 90:1–7

    Article  Google Scholar 

  106. Matsubara T, Ochiai T, Hayashida M, Akutsu T, Nacher JC (2019) Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles. J Bioinform Comput Biol 17(03):1940007

    Article  Google Scholar 

  107. Lamy JB, Sekar B, Guezennec G, Bouaud J, Séroussi B (2019) Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach. Artif Intell Med 94:42–53

    Article  Google Scholar 

  108. Sabol P, Sinčák P, Hartono P, Kočan P, Benetinová Z, Blichárová A, Verbóová Ľ, Štammová E, Sabolová-Fabianová A, Jašková A (2020) Explainable classifier for improving the accountability in decision-making for colorectal cancer diagnosis from histopathological images. J Biomed Inform 109:103523

    Google Scholar 

  109. Gadgil C, Yeckel A, Derby JJ, Hu WS (2004) A diffusion–reaction model for DNA microarray assays. J Biotechnol 114(1–2):31–45

    Article  Google Scholar 

  110. Wang L, Chu F, Xie W (2007) Accurate cancer classification using expressions of very few genes. IEEE/ACM Trans Comput Biol Bioinf 4(1):40–53

    Article  Google Scholar 

  111. Mahmud M, Kaiser MS, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063–2079

    Article  MathSciNet  Google Scholar 

  112. Jing L, Ng MK, Liu Y (2009) Construction of gene networks with hybrid approach from expression profile and gene ontology. IEEE Trans Inf Technol Biomed 14(1):107–118

    Article  Google Scholar 

  113. Cho H, Levy D (2018) Modeling the chemotherapy-induced selection of drug-resistant traits during tumor growth. J Theor Biol 436:120–134

    Article  MathSciNet  MATH  Google Scholar 

  114. Zhang X, Han Y, Wu L, Wang Y (2016) State estimation for delayed genetic regulatory networks with reaction–diffusion terms. IEEE Trans Neural Netw Learn Syst 29(2):299–309

    Article  MathSciNet  Google Scholar 

  115. Song X, Wang M, Song S, Ahn CK (2019) Sampled-data state estimation of reaction diffusion genetic regulatory networks via space-dividing approaches. IEEE/ACM Trans Comput Biol Bioinform

    Google Scholar 

  116. Anguita-Ruiz A, Segura-Delgado A, Alcalá R, Aguilera CM, Alcalá-Fdez J (2020) EXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research. PLoS Comput Biol 16(4):e1007792

    Google Scholar 

  117. Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th international conference on data science and advanced analytics (DSAA). IEEE, Oct 2018, pp 80–89

    Google Scholar 

  118. Preece A, Harborne D, Braines D, Tomsett R, Chakraborty S (2018) Stakeholders in explainable AI. arXiv preprint arXiv:1810.00184

  119. Mishra S, Mahanty C, Dash S, Mishra BK (2019) Implementation of BFS-NB hybrid model in intrusion detection system. In: Recent developments in machine learning and data analytics. Springer, Singapore, pp 167–175

    Google Scholar 

  120. Goudet O, Kalainathan D, Caillou P, Guyon I, Lopez-Paz D, Sebag M (2018) Learning functional causal models with generative neural networks. In: Explainable and interpretable models in computer vision and machine learning. Springer, Cham, pp 39–80

    Google Scholar 

  121. Lopez-Paz D, Nishihara R, Chintala S, Scholkopf B, Bottou L (2017) Discovering causal signals in images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6979–6987

    Google Scholar 

  122. Byrne RM (2019) Counterfactuals in Explainable Artificial Intelligence (XAI): evidence from human reasoning. In: IJCAI, Aug 2019, pp 6276–6282

    Google Scholar 

  123. Bennetot A, Laurent JL, Chatila R, Díaz-Rodríguez N (2019) Towards explainable neural-symbolic visual reasoning. arXiv preprint arXiv:1909.09065

  124. Garcez ADA, Gori M, Lamb LC, Serafini L, Spranger M, Tran SN (2019) Neural-symbolic computing: an effective methodology for principled integration of machine learning and reasoning. arXiv preprint arXiv:1905.06088

  125. Marra G, Giannini F, Diligenti M, Gori M (2019) Integrating learning and reasoning with deep logic models. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Cham, Sept 2019, pp 517–532

    Google Scholar 

  126. Donadello I, Serafini L, Garcez ADA (2017) Logic tensor networks for semantic image interpretation. arXiv preprint arXiv:1705.08968

  127. Doran D, Schulz S Besold TR (2017) What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794

  128. Kelley K, Clark B, Brown V, Sitzia J (2003) Good practice in the conduct and reporting of survey research. Int J Qual Health Care 15(3):261–266

    Article  Google Scholar 

  129. Wachter S, Mittelstadt B, Floridi L (2017) Why a right to explanation of automated decision-making does not exist in the general data protection regulation. Int Data Privacy Law 7(2):76–99

    Article  Google Scholar 

  130. Mishra S, Tripathy HK, Panda AR (2018) An improved and adaptive attribute selection technique to optimize dengue fever prediction. Int J Eng Technol 7:480–486

    Article  Google Scholar 

  131. Orekondy T, Schiele B, Fritz M (2019) Knockoff nets: stealing functionality of black-box models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4954–4963

    Google Scholar 

  132. Oh SJ, Schiele B, Fritz M (2019) Towards reverse-engineering black-box neural networks. In: Explainable AI: interpreting, explaining and visualizing deep learning. Springer, Cham, pp 121–144

    Google Scholar 

  133. Eykholt K, Evtimov I, Fernandes E, Li B, Rahmati A, Xiao C, Prakash A, Kohno T, Song D (2018) Robust physical-world attacks on deep learning visual classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1625–1634

    Google Scholar 

  134. Goodfellow I, Papernot N, McDaniel P, Feinman R, Faghri F, Matyasko A, Hambardzumyan K, Juang YL, Kurakin A, Sheatsley R, Garg A (2016) Cleverhans v0. 1: an adversarial machine learning library. arXiv preprint arXiv:1610.00768, 1

  135. Xiao H, Biggio B, Nelson B, Xiao H, Eckert C, Roli F (2015) Support vector machines under adversarial label contamination. Neurocomputing 160:53–62

    Article  Google Scholar 

  136. Folorunso SO, Awotunde JB, Banjo OO, Ogundepo EA, Adeboye NO (2021) Comparison of active COVID-19 cases per population using time-series models. Int J E-Health Med Commun (IJEHMC) 13(2):1–21

    Google Scholar 

  137. Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (GANs): a survey. IEEE Access 7:36322–36333

    Article  Google Scholar 

  138. Charte D, Charte F, García S, del Jesus MJ, Herrera F (2018) A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines. Inform Fusion 44:78–96

    Article  Google Scholar 

  139. Baumgartner CF, Koch LM, Tezcan KC, Ang JX, Konukoglu E (2018) Visual feature attribution using wasserstein gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8309–8319

    Google Scholar 

  140. Biffi C, Oktay O, Tarroni G, Bai W, Marvao AD, Doumou G, Rajchl M, Bedair R, Prasad S, Cook S, O’Regan D (2018) Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, Sept 2018, pp 464–471

    Google Scholar 

  141. Abiodun KM, Awotunde JB, Aremu DR, Adeniyi EA (2022) Explainable AI for fighting COVID-19 pandemic: opportunities, challenges, and future prospects. In: Computational intelligence for COVID-19 and future pandemics. Springer, Singapore, pp 315–332

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde & Ghaniyyat Bolanle Balogun

  2. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Emmanuel Abidemi Adeniyi

  3. Department of Agricultural Extension and Rural Development, Landmark University, Omu-Aran, Nigeria

    Gbemisola Janet Ajamu

  4. Department of Computer Science, Federal Polytechnic, Offa, Nigeria

    Fatimoh Abidemi Taofeek-Ibrahim

Authors
  1. Joseph Bamidele Awotunde
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Abidemi Adeniyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gbemisola Janet Ajamu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ghaniyyat Bolanle Balogun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fatimoh Abidemi Taofeek-Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joseph Bamidele Awotunde .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Sushruta Mishra

  2. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. KIET Group of Institutions, Ghaziabad, Delhi-NCR, India

    Akash Kumar Bhoi

  4. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  5. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Juan M. Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Awotunde, J.B., Adeniyi, E.A., Ajamu, G.J., Balogun, G.B., Taofeek-Ibrahim, F.A. (2022). Explainable Artificial Intelligence in Genomic Sequence for Healthcare Systems Prediction. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_19

Download citation

Publish with us

Policies and ethics

Search

Navigation