Skip to main content
SpringerLink
Log in

Implementation of HBEA for Tumor Cell Prediction Using Gene Expression and Dose Response

  • Conference paper
  • First Online:
Intelligent Communication Technologies and Virtual Mobile Networks (ICICV 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 171))

Included in the following conference series:

Abstract

An important aspect of sustainable drug development is drug-target interaction. In cancer cell lines, the drug response target ratio is critical. It is important to estimate the drug reaction in a cancer cell line. In prior research, we employed ensemble algorithms with voting methods to predict medication response and achieved 97.5% accuracy. A hybrid ensemble algorithm for the revised drug response (HBEA) method is developed to improve drug-target strategy in cell lines. Rather than generating several homogeneous weak learners to generate a single model in the ensemble, this enhanced algorithm uses a diverse collection of weak learners such as random forest, Naive Bayes, and decision tree to create a strong meta-classifier. Cross-validation of hard and soft data would be used to accomplish this. The concentrations of various drugs are used as inputs, and the cell line predicts the relevant drug response. The goal of this enhanced ensemble algorithm is to suggest a new medicine based on a single licensed drug or a combination of drugs. This approach increased the drug responsiveness from 97.5 to 100%, according to our findings. The proposed method is applied in an open-source and freely available at https://decrease.fimm.fi.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Ashley EA (2015) The precision medicine initiative: a new national effort. JAMA 313:2119–2120

    Article  Google Scholar 

  2. Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372:793–795

    Article  Google Scholar 

  3. Adam G, Rampášek L, Safikhani Z, Smirnov P, HaibeKains B, Goldenberg A (2020) Machine learning approaches to drug response prediction: challenges and recent progress, NPJ Precis Oncol

    Google Scholar 

  4. Wang L, Li X, Zhang L, Gao Q (2017) Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization. BMC Cancer 17:513

    Article  Google Scholar 

  5. Lu X, Gu H, Wang Y, Wang J, Qin P (2019) Autoencoder based feature selection method for classification of anticancer drug response. Front Genet 10:233

    Article  Google Scholar 

  6. Azuaje F (2017) Computational models for predicting drug responses in cancer research. Brief Bioinf 18:820–829

    Google Scholar 

  7. Costello JC et al (2014) A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 32:1202

    Article  Google Scholar 

  8. Lee W-H, Loo C-Y, Daniela Traini P, Young M (2015) Inhalation of nanoparticlebased drug for lung cancer treatment: advantages and challenges. Asian J Pharma Sci

    Google Scholar 

  9. Patton JS, Fishburn CS, Weers JG (2004) The lungs as a portal of entry for systemic drug delivery. Proc Am Thorac Soc 1:338–344

    Article  Google Scholar 

  10. Schanker LS, Less MJ (1977) Lung pH and pulmonary absorption of nonvolatile drugs in the rat. Drug Metabol Dispos 5:174–178

    Google Scholar 

  11. Lai Y-H, Chen W-N, Hsu T-C, Lin C, Tsao Y, Semon W (2020) Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning. Sci Rep 10(1):1–11

    Google Scholar 

  12. Doppalapudi S, Qiu RG, Badr Y, Lung cancer survival period prediction and understanding: deep learning

    Google Scholar 

  13. SEER Program (2019) National Cancer Institute (NCI), SEER Incidence Data, 1975–2017. Available https://seer.cancer.gov/data/

  14. Fangfang Xia “A cross-study analysis of drug response prediction in cancer cell lines”, 23(1), 2022, 1–12.

    Google Scholar 

  15. Yıldırım MA et al (2007) Drug—target network. Nat Biotechnol 25:1119

    Article  Google Scholar 

  16. Heba E-B, Attia A-F, Nawal E-F, Torkey H (2021) Efficient machine learning model for predicting drug-target interactions with case study for Covid-19. Comput Biol Chem

    Google Scholar 

  17. Ding H et al (2014) Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief Bioinform 15(5):734–747

    Article  Google Scholar 

  18. Nath A, Kumari P, Chaube R (2018) Prediction of human drug targets and their interactions using machine learning methods: current and future perspectives. Methods Mol Biol 1762:21–30

    Article  Google Scholar 

  19. Ezzat A et al (2018) Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 20(4):1337–1357

    Article  Google Scholar 

  20. Sachdev K, Gupta MK (2019) A comprehensive review of feature-based methods for drug-target interaction prediction. J Biomed Inform 93:103159

    Article  Google Scholar 

  21. Zhou L et al (2019) Revealing drug-target interactions with computational models and algorithms. Molecules 24(9):1714

    Google Scholar 

  22. Zhang W et al (2019) Recent advances in machine learning-based drug-target interaction prediction. Curr Drug Metab 20(3):194–202

    Article  Google Scholar 

  23. Thafar M, Raies AB, Albaradei S, Essack M, Bajic VB (2019) Comparison study of computational prediction tools for drug–target binding affinities. Front Chem 7:782

    Article  Google Scholar 

  24. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, Hoboken, NJ

    Book  MATH  Google Scholar 

  25. Windeatt T, Roli F (eds) (2003) Multiple classifier systems, in Lecture Notes in Computer Science, vol 2709, Springer

    Google Scholar 

  26. Roli F, Kittler J, Windeatt T (eds) (2004) Multiple classifier systems, in Lecture Notes in Computer Science, vol 3077, Springer

    Google Scholar 

  27. Zhou Z-H (2012) Ensemble methods: foundations and algorithms (Chapman & Hall/CRC data mining and knowledge discovery series). Chapman & Hall/CRC, Boca Raton, FL

    Book  Google Scholar 

  28. Brazil P, Giraud-Carrier C, Soares C (2009) Meta-learning: applications to data mining. Springer, Berlin Heidelberg

    Book  Google Scholar 

  29. Polikar R (2006) Ensemble-based systems in decision making. IEEE Circ Syst Mag 6(3):21–45

    Article  Google Scholar 

  30. Agrawal SM, Narayanan R, Polepeddi L, Choudhary A (2012) Lung cancer survival prediction using ensemble data mining on SEER data. Sci Progr 20(1):29–42

    Google Scholar 

  31. Safiyari A, Javidan R (2017) Predicting lung cancer survivability using ensemble learning methods. Intell Syst Conf (IntelliSys)

    Google Scholar 

  32. Costello JC, Heiser LM, Georgii E et al (2014) A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 32(12):1202–1212

    Article  Google Scholar 

  33. Zhang C, Ma Y (2012) Ensemble machine learning: methods and applications, Springer

    Google Scholar 

  34. Lianga G, Fanb W, Luo H, Zhua X (2020) The emerging roles of artificial intelligence in cancer drug development and precision therapy, vol 128, p 110255

    Google Scholar 

  35. http://www.definitions.net

  36. Iorio F, Knijnenburg TA, Vis DJ et al (2016) A landscape of pharmacogenomic interactions in cancer. Cell 166(3):740–754

    Article  Google Scholar 

  37. Borg I, Groenen PJF (2005) Modern multidimensional scaling: theory and applications, 2nd edn. Springer, New York

    Google Scholar 

  38. Santini S, Jain R (1999) Similarity measures. IEEE Trans Patt Anal Mach Intel 21(9):871–883

    Google Scholar 

  39. Smith F, Waterman MS (1981) Identification of common molecular subsequences. J Mole Biol 147(1):195–197

    Article  Google Scholar 

  40. Lipman J, Pearson WR (1985) Rapid and sensitive protein similarity searches. Science 227(4693):1435–1441

    Article  Google Scholar 

  41. Gish AW, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mole Biol 215(3):403–410

    Google Scholar 

  42. Stanfield Z et al (2017) Drug response prediction as a link prediction problem. Sci Rep 7:40321

    Article  Google Scholar 

  43. Yang J, Li A, Li Y, Guo X, Wang M (2019) A novel approach for drug response prediction in cancer cell lines via network representation learning. Bioinformatics

    Google Scholar 

  44. Zhang N et al (2015) Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model. PLoS Comput Biol 11:e1004498

    Article  Google Scholar 

  45. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Durbourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  46. Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning, 2nd edn. Springer, New York/Berlin/Heidelberg

    MATH  Google Scholar 

  47. Duda R, Hart P, Stork D (2001) Pattern classification. John Wiley & Sons

    Google Scholar 

  48. Efron B, Tibshirani R (1993) An Introduction to the Bootstrap. Chapman & Hall

    Google Scholar 

  49. Bhardwaj R, Hooda N (2009) Prediction of pathological complete response after Neoadjuvant Chemotherapy for breast cancer using ensemble machine learning, pp 2352–9148

    Google Scholar 

  50. Chen JIZ, Hengjinda P (2021) Early prediction of coronary artery disease (CAD) by machine learning method-a comparative study. J Artif Intell 3(01):17–33

    Google Scholar 

  51. Manoharan S (2019) Study on Hermitian graph wavelets in feature detection. J Soft Comput Paradigm (JSCP) 1(1):24–32

    Google Scholar 

  52. Kening Li, Yuxin Du, Lu Li, Dong-Qing Wei. “Bioinformatics Approaches for Anti-cancer Drug Discovery” , Current Drug Targets, 2019.

    Google Scholar 

  53. Kuenzi BM, Park J, Fong SH, Sanchez KS, Lee J, Kreisberg JF, Ma J, Ideker T (2020) Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell

    Google Scholar 

  54. Anithaashri TP, Rajendran PS, Ravichandran G (2022) Novel intelligent system for medical diagnostic applications using artificial neural network, Lecture Notes on Data Engineering and Communications Technologiesthis link is disabled, vol 101, pp 93–101

    Google Scholar 

  55. Niroomand N, Bach C, Elser M (2021) Segment-based CO emission evaluations from passenger cars based on deep learning techniques. IEEE Access

    Google Scholar 

  56. Prajwal K, Tharun K, Navaneeth P, Anand Kumar M (2022) Cardiovascular disease prediction using machine learning. In: International conference on innovative trends in information technology (ICITIIT)

    Google Scholar 

  57. lanevski A, Giri AK, Gautam P, Kononov A, Potdar S, Saarela J, Wennerberf K, Aittokallio T (2019) Prediction of drug combination effects with a minimal set of experiments, vol 1, pp. 568–577

    Google Scholar 

  58. Baptista D, Ferreira PG, Rocha M (2020) Deep learning for drug response prediction in cancer. Brief Bioinform

    Google Scholar 

  59. Rajendran PS, Kartheeswari KR (2022) Anti-cancer drug response prediction system using stacked ensemble approach, Lecture Notes in Networks, vol 436, pp 205–218

    Google Scholar 

Download references

Acknowledgements

This research is funded by the Indian Council of Medical Research (ICMR). (Sanction no: ISRM/12(125)/2020 ID NO.2020-5128 dated 10/01/21).

Author information

Authors and Affiliations

  1. Computer Science and Engineering, School of Computing, Hindustan Institute of Technology and Science, Chennai, India

    P. Selvi Rajendran & K. R. Kartheeswari

Authors
  1. P. Selvi Rajendran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. R. Kartheeswari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Selvi Rajendran .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India

    G. Rajakumar

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

  3. ISEG—University of Lisbon, Lisbon, Portugal

    Álvaro Rocha

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rajendran, P.S., Kartheeswari, K.R. (2023). Implementation of HBEA for Tumor Cell Prediction Using Gene Expression and Dose Response. In: Rajakumar, G., Du, KL., Rocha, Á. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-99-1767-9_46

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-1767-9_46

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1766-2

  • Online ISBN: 978-981-99-1767-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation