Genetic Algorithm for Biomarker Search Problem and Class Prediction

Автор: Shabia Shabir Khan, S.M.K. Quadri, M.A. Peer

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 9 vol.8, 2016 года.

Бесплатный доступ

In the field of optimization, Genetic Algorithm that incorporates the process of evolution plays an important role in finding the best solution to a problem. One of the main tasks that arise in the medical field is to search a finite number of factors or features that actually affect or predict the survival of the patients especially with poor prognosis disease, thus helping them in early diagnosis. This paper discusses the various steps that are performed in genetic algorithm and how it is going to help in extracting knowledge out of high dimensional medical dataset. The more the attributes or features, the more difficult it is to correctly predict the class of that sample or instance. This is because of inefficient, useless, noisy attributes in the dataset. So, here the main aim is to search the features or genes that can strongly predict the class of subject (patient) i.e. healthy or cancerous and thus help in early detection and treatment.

Еще

Genetic Algorithm (GA), Artificial Neural Network (ANN), Fitness Function, Feature Selection, Classification

Короткий адрес: https://sciup.org/15010857

IDR: 15010857

Список литературы Genetic Algorithm for Biomarker Search Problem and Class Prediction

  • Kampouropoulos, Konstantinos, et al. "A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting.Advances in Electrical and computer engineering”. Volume 14, number 1 (2014).
  • Tahmasebi, Pejman, and Ardeshir Hezarkhani. "A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation." Computers & geosciences 42 (2012): 18-27.
  • Fakhreddine O. Karray, Clarence De Silva, “Soft Computing and Intelligent Systems Design- Theory, Tools and Applications”, Pearson Education, 2009.
  • S.N.Sivanandam, S.N.Deepa, “Principles of Soft Computing”, Wiley India Edition,2007
  • Hanafy, Tharwat OS. "A modified algorithm to model highly nonlinear system."J Am Sci 6.12 (2010): 747-759.
  • Ge, Shuzhi Sam, and Cong Wang. "Adaptive neural control of uncertain MIMO nonlinear systems." Neural Networks, IEEE Transactions on 15.3 (2004): 674-692.
  • Hanafy, Tharwat OS. "A modified algorithm to model highly nonlinear system."J Am Sci 6.12 (2010): 747-759.
  • Goldberg D.E., “Genetic Algorithms in Search, Optimisation, and Machine Learning”, Addison-Wesly, Reading, 1989.
  • Michalewicz, Z., “Genetic Algorithms +Data Structures = Evolution Programs”, Springer, 1996.
  • Vose M.D., “The Simple Genetic Algorithm: Foundations and Theory (Complex Adaptive Systems)”, Bradford Books, 1999.
  • Matlab, “Global Optimization Toolbox User's Guide”, The MathWorks, Inc, Revised 2015
  • Yvan Saeys,Inaki Inza and Pedro Larranaga,” A review of feature selection techniques in bioinformatics Bioinformatics” , BIOINFORMATICS REVIEW, Gene expression, Vol. 23 no. 19 2007, pages 2507–2517 , 2007
  • Daelemans,W., et al., “Combined optimization of feature selection and algorithm parameter interaction in machine learning of language:A review of feature selection techniques”,Proceedings of the 14th European Conference on Machine Learning (ECML-2003), pp. 84–95
  • Li,T., et al. (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression.Bioinformatics, 20, 2429–2437
  • Petricoin,E., et al. (2002) Use of proteomics patterns in serum to identify ovarian cancer. The Lancet, 359, 572–577.
  • Kim, Kyoung-jae, and Ingoo Han. "Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index." Expert systems with Applications 19.2 (2000): 125-132.
  • Dilip Kumar Choubey, Sanchita Paul, Joy Bhattacharjee “Soft Computing Approaches for Diabetes Disease Diagnosis: A Survey”, International Journal of Applied Engineering Research, Vol. 9, pp. 11715-11726, 2014
  • Choubey, Dilip Kumar, and Sanchita Paul. "GA_MLP NN: A Hybrid Intelligent System for Diabetes Disease Diagnosis." (2016).
  • V.S.R. Kumari, P.R. Kumar,” Classification of cardiac arrhythmia using hybrid genetic algorithm optimisation for multi-layer perceptron neural network”, International Journal of Biomedical Engineering and Technology, Volume 20, Issue 2, 2016
  • Sudhakar, M., J. Albert Mayan, and N. Srinivasan. "Intelligent Data Prediction System Using Data Mining and Neural Networks." Proceedings of the International Conference on Soft Computing Systems. Springer India, 2016.
  • Ahmadizar, Fardin, et al. "Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm."Engineering Applications of Artificial Intelligence 39 (2015): 1-13.
  • Melanie Mitchell(1996), An Introduction to Genetic Algorithms, A Bradford Book, The MIT Press, Cambridge, Massachusets Institute of Technology, 1996
  • Ahmad, Fadzil, et al. "A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer." Pattern Analysis and Applications 18.4 (2015): 861-870.
  • Nianyi Chen, Wencong Lu, Jie Yang, Guozheng Li, “Support Vector Machine in Chemistry”, World Scientific ,Chap 4, pp.61, 2004
  • Khan, Sheema, et al. "MicroRNA-145 targets MUC13 and suppresses growth and invasion of pancreatic cancer." Oncotarget 5.17 (2014): 7599.
  • Moschopoulos, Charalampos, et al. "A genetic algorithm for pancreatic cancer diagnosis." Engineering Applications of Neural Networks. Springer Berlin Heidelberg, 2013. 222-230.
  • Svetlana S. Aksenova, “Machine Learning with WEKA”, WEKA Explorer Tutorial., 2004
  • Zhang L, Farrell JJ, Zhou H, Elashoff D et al. Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. Gastroenterology,138(3):949-57, Mar 2010
  • SM Kalami Heris, H Khaloozadeh , “Non-dominated sorting genetic filter a multi-objective evolutionary particle filter”, Intelligent Systems (ICIS), Iranian Conference 2014
  • Kumari, B., Swarnkar, T., “Filter versus Wrapper Feature Subset Selection in Large Dimensionality Micro array: A Review”, IJCSIT, Vol.2 (3), pp. 1048-1053, 2011
  • Amato, F.,Lopez, A., Maria, E.P.M.,Vanhara, P.,Hampl, A., Havel, J., “Artificial neural networks in medical diagnosis”, J Appl Biomed, 11:47-58, 2013
  • Erguzel, Turker Tekin, et al. "Feature Selection and Classification of Electroencephalographic Signals An Artificial Neural Network and Genetic Algorithm Based Approach." Clinical EEG and neuroscience 46.4 (2015): 321-326.
Еще
Статья научная