International Journal of System & Software Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Proposed Paper on Gene Expression with PSO and K-Mean


Affiliations
1 Computer Science Engineering, ITM University, Gwalior, Madhya Pradesh, India
     

   Subscribe/Renew Journal


Profiles of gene expression, which is in cell state present at molecular level, have huge potential medical diagnosis tool. Compared to the various genes conclude, presented training information sets usually have a fairly small sample size in the cancer type classification. These training information limitations constitute a challenge to various classification technologies. A reliable selection technique for genes relevant for sample classification is required in order to the speed up processing rate, decrease predictive error rate, and to avoid incomprehensibility because of genes investigated many number.


Keywords

Gene Expression, PSO, K-Means.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Torres-Avilés, F., Romeo, J. S., López-Kleine, L. (2014). Data mining and influential analysis of gene expression data for plant resistance gene identification in tomato (Solanum lycopersicum). Electronic Journal of Biotechnology, March, 17(2), 79-82.

  • Lu., H., Yanga, L., Yan, K., Xue, K., & Gao, Z. (2016). A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 228(C), 270-276.

  • Lu, H., Chen, J., Yana., K., Jina, Q., Xue, Y., & Gao, Z. (2016). A Hybrid Feature Selection Algorithm for Gene Expression Data Classification. Neurocomputing.

  • Sarkar, S., Roy, A., & Purkayastha, B. S. (2013). Application of particle swarm optimization in data clustering: A survey. International Journal of Computer Applications, March, 65(25), 38-46.

  • Wang, H., & Lv, X. (2016). Mining Raw Gene Expression Microarray Data for Analyzing Synchronous and Meta-chronous Liver Metastatic Lesions from Colorectal Cancer. 9th International Congress on Image and Signal Processing, Bio-Medical Engineering and Informatics, (pp. 1826-1831).

  • Mallik, S., Mukhopadhyay, A., & Maulik, U. (2014). RANWAR: Rank-Based Weighted Association Rule Mining from Gene Expression and Methylation Data. IEEE Transactions on Nano Bios, March, 14(1).

  • Chen, H., Zhao, H., Shen, J., Zhou, R., & Zhou, Q. (2015). Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection. IEEE International Congress on Big Data, (pp. 134-141).

  • Deng, S., Yuan, C., Yang, J., & Zhou, A. (2017). Distributed Mining for Content Filtering Function based on Simulated Annealing and Gene Expression Programming in Active Distribution Network. IEEE Access, Electronics, Intelligent Control and Energy Systems, January, (pp. 2319-2328).

  • Mishra, A., Biswal, B. S., Mohapatra, A., & Vipsita, S. (2017). Biclustering of Gene Expression Patterns with an Advanced Overlapping Control Strategy. 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems.

  • Xie, R., Quitadamo, A., Cheng, J., & Shi, X. (2016). A Predictive Model of Gene Expression Using a Deep Learning Framework. IEEE International Conference on Bioinformatics and Biomedicine, (pp. 676-681).

  • Priya, P. P. A., & Lawrance, R. (2016). Algorithm for Clustering Analysis of Gene Expression Data using Map Reduce Framework. International Conference on Computer Technologies and Intelligent Data Engineering.

  • Alagukumar, S., & Lawrance, R. (2016). Classification of Microarray Gene Expression Data using Associative Classification. International Conference on Computer Technologies and Intelligent Data Engineering.

  • Xu, T., Su, N., Wang, R., & Song, L. (2015). Gene Selection for Cancer Clustering Analysis Based on Expression Data. 4th International Conference on Computer Science and Network Technology, (pp. 516-519).

  • Zakaria, W., Kot, Y., & Ghale, F. F. M. (2015). Min CAR-Classifier for Classifying Lung Cancer Gene Expression Dataset. IEEE 7th International Conference on Intelligent Computing and Information Systems, (pp. 143-148).


Abstract Views: 339

PDF Views: 1




  • A Proposed Paper on Gene Expression with PSO and K-Mean

Abstract Views: 339  |  PDF Views: 1

Authors

Somesh Shakya
Computer Science Engineering, ITM University, Gwalior, Madhya Pradesh, India
Anshul Atre
Computer Science Engineering, ITM University, Gwalior, Madhya Pradesh, India

Abstract


Profiles of gene expression, which is in cell state present at molecular level, have huge potential medical diagnosis tool. Compared to the various genes conclude, presented training information sets usually have a fairly small sample size in the cancer type classification. These training information limitations constitute a challenge to various classification technologies. A reliable selection technique for genes relevant for sample classification is required in order to the speed up processing rate, decrease predictive error rate, and to avoid incomprehensibility because of genes investigated many number.


Keywords


Gene Expression, PSO, K-Means.

References