Home Neural networks as a tool for modeling of biological systems
Article
Licensed
Unlicensed Requires Authentication

Neural networks as a tool for modeling of biological systems

  • Ryszard Tadeusiewicz EMAIL logo
Published/Copyright: September 2, 2015
Become an author with De Gruyter Brill

Abstract

Neural networks become very popular as a tool for modeling of numerous systems, including technological, economical, sociological, psychological, and even political ones. On the contrary, neural networks are models of neural structures and neural processes observed in a real brain. However, for modeling of real neural structures and real neural processes occurring in a living brain, neural networks are too simplified and too primitive. Nevertheless, neural networks can be used for modeling the behavior of many biological systems and structures. Such models are not useful for explanation, taking into account the biological systems and processes, but can be very useful for the analysis of such system behavior, including the prognosis of future results of selected activities (e.g. the prognosis of results of different therapies for modeled illnesses). In this paper, selected examples of such models and their applications are presented.


Corresponding author: Ryszard Tadeusiewicz, Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30 Ave., 30-059 Krakow, Poland, E-mail:

Acknowledgments

This work was supported by the AGH University of Science and Technology (Grant No 11.11.120.612).

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Tadeusiewicz R, Chaki R, Chaki N. Exploring neural networks with C#. Boca Raton: CRC Press, Taylor & Francis Group, 2014.Search in Google Scholar

2. Wu JJ, Zhang Y. ECG identification based on neural networks. 11th Int Comput Conf Wavelet Active Media Technol Inf Process IEEE 2014:92–6.10.1109/ICCWAMTIP.2014.7073368Search in Google Scholar

3. Shen W, Yang F, Mu W, Yang C, Yang X, Tian J. Automatic localization of vertebrae based on convolutional neural networks. Med Imaging 2015 Image Process SPIE. Proc SPIE Prog Biomed Optics Imaging 9413, 2015, 94132E (6 pp.).10.1117/12.2081941Search in Google Scholar

4. De A, Guo C. An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing. Int J Mach Learn Cybernet 2014;5:543–51.10.1007/s13042-013-0205-1Search in Google Scholar

5. Morra L, Delsanto S, Lamberti F. Methods for neural-network-based segmentation of magnetic resonance images. In: Akay M, editor. Handbook of neural engineering. Chapter 10. Piscataway, NJ, USA: IEEE Press, 2007:173–92.10.1002/9780470068298.ch10Search in Google Scholar

6. Li G, Liu T, Li T, Inoue Y, Yi J. Neural network-based gait assessment using measurements of a wearable sensor system. Proc 2014 IEEE Int Conf Robot Biomimet 2014:1673–78.10.1109/ROBIO.2014.7090575Search in Google Scholar

7. Azzerboni B, Ipsale M, La Foresta F, Morabito FC. Neural networks and time-frequency analysis of surface electromyographic signals for muscle cerebral control. In: Akay M, editor. Handbook of neural engineering. Chapter 7. Piscataway, NJ, USA: IEEE Press, 2007:131–55.10.1002/9780470068298.ch8Search in Google Scholar

8. Inbarani HH, Nizar Banu PK, Azar AT. Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput Appl 2014;25:793–806.10.1007/s00521-014-1552-xSearch in Google Scholar

9. Tedesco M, Frega M, Pastorino L, Massobrio P, Martinoia S. 3D engineered neural networks coupled to micro-electrode based devices: a new experimental model for neurophysiological applications. XVIII AISEM Annu Conf Proc IEEE 2015:4–6.10.1109/AISEM.2015.7066778Search in Google Scholar

10. Kawaguchi M, Ishii N, Jimbo T. Analog learning neural network using two-stage mode by multiple and sample hold. Int J Soft Innov 2014;2:61–72.10.4018/ijsi.2014010105Search in Google Scholar

11. Beheshti Z, Shamsuddin SM, Beheshti E, Yuhaniz SS. Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft Comput 2014;18:2253–70.10.1007/s00500-013-1198-0Search in Google Scholar

12. Gutierrez A. The PSO algorithm and the diagnosis of multiple sclerosis using artificial neural networks. Proc 2014 Annu Global Online Conf Inf Comput Technol IEEE Comput Soc 2014:5–10.10.1109/GOCICT.2014.24Search in Google Scholar

13. Harikumar R, Vinoth Kumar B. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Int J Imaging Syst Technol 2015;25:33–40.10.1002/ima.22118Search in Google Scholar

14. Utomo CP, Kardiana A, Yuliwulandari R. Breast cancer diagnosis using artificial neural networks with extreme learning techniques. Int J Adv Res Artif Intell 2014;3:10–4.10.14569/IJARAI.2014.030703Search in Google Scholar

15. Zaman NA, Rahman WE, Jumaat AK, Yasiran SS. Classification of breast abnormalities using artificial neural network. Int Conf Math Eng Ind Appl 2014:1660–7.10.1063/1.4915671Search in Google Scholar

16. Hamedi M, Salleh SH, Noor AM, Mohammad Rezazadeh I. Neural network-based three-class motor imagery classification using time-domain features for BCI applications. 2014 IEEE Region 10 Symp 2014:204–7.10.1109/TENCONSpring.2014.6863026Search in Google Scholar

17. Slim MA, Abdelkrim A, Benrejeb M. Handwriting velocity modeling by sigmoid neural networks with Bayesian regularization. Int Conf Elect Sci Technol Maghreb Tunis 2014:7–12.10.1109/CISTEM.2014.7077076Search in Google Scholar

18. Vincent I, Kwon KR, Lee SH, Seok Moon KS. Acute lymphoid leukemia classification using two-step neural network classifier. 21st Korea Japan Joint Workshop Front Comput Vision 2015:123–7.10.1109/FCV.2015.7103739Search in Google Scholar

19. Gao X, Huang T, Wang Z, Xiao M. Exploiting a modified gray model in back propagation neural networks for enhanced forecasting. Cognit Comput 2014;6:331–7.10.1007/s12559-014-9247-2Search in Google Scholar

20. Wu TH, Pang GK, Kwong EW. Predicting systolic blood pressure using machine learning. 7th Int Conf Inf Automat Sustain IEEE 2014:1–6.10.1109/ICIAFS.2014.7069529Search in Google Scholar

21. Lin CC, Chan HH, Huang CY, Yang NS. Predictive models for pre-operative diagnosis of rotator cuff tear: a comparison study of two methods between logistic regression and artificial neural network. Appl Mech Mater 2014;595:263–8.10.4028/www.scientific.net/AMM.595.263Search in Google Scholar

22. Hu J, Hou ZG, Chen YX, Kasabov N, Scott N. EEG-based classification of upper-limb ADL using SNN for active robotic rehabilitation. 5th IEEE RAS EMBS Int Conf Biomed Robot Biomechatron 2014:409–14.10.1109/BIOROB.2014.6913811Search in Google Scholar

23. Korovin EN, Trukhachev AS, Fursova EA. Neural-network modelling choice of treatment tactics for patients with chronic heart failure against operated acquired heart diseases. Syst Anal Control Biomed Syst 2014;13:916–20.Search in Google Scholar

24. Ning Y, Han LL, Xiao ZR, Liu BG. Force feedback time prediction based on neural network of MIS Robot with time delay. Proc 2014 IEEE Int Conf Robot Biomimet 2014:2703–8.10.1109/ROBIO.2014.7090751Search in Google Scholar

Received: 2015-6-29
Accepted: 2015-7-16
Published Online: 2015-9-2
Published in Print: 2015-9-1

©2015 by De Gruyter

Downloaded on 6.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/bams-2015-0021/html
Scroll to top button