Call for Papers:Vol.11 Issue.3

Submission
Last date		 28-Jun-2025
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-11,Issue-3. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Classification of Gene Expression Data by Gene Combination using Fuzzy Logic
PaperId: :  1137
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 1 Issue 2 2015
DUI:    16.0415/IJARIIE-1137
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Daxa GhadiyaCGPIT,BARDOLI
Prof. Ankit Kharwar CGPIT,BARDOLI
Prof. Monali GandhiCGPIT,BARDOLI

Abstract

Data mining
Feature selection, classification, clustering, T-test, Cancer classification, gene expression, fuzzy, neural networks, supports vector machines
The goal of microarray experiments is to identify genes that are differentially transcribed with respect to different biological conditions of cell cultures and samples. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is improved/ modified in this report to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes mare filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi. "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic" International Journal Of Advance Research And Innovative Ideas In Education Volume 1 Issue 2 2015 Page 43-48
MLA Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi. "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic." International Journal Of Advance Research And Innovative Ideas In Education 1.2(2015) : 43-48.
APA Daxa Ghadiya, Prof. Ankit Kharwar , & Prof. Monali Gandhi. (2015). Classification of Gene Expression Data by Gene Combination using Fuzzy Logic. International Journal Of Advance Research And Innovative Ideas In Education, 1(2), 43-48.
Chicago Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi. "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic." International Journal Of Advance Research And Innovative Ideas In Education 1, no. 2 (2015) : 43-48.
Oxford Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi. 'Classification of Gene Expression Data by Gene Combination using Fuzzy Logic', International Journal Of Advance Research And Innovative Ideas In Education, vol. 1, no. 2, 2015, p. 43-48. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Classification_of_Gene_Expression_Data_by_Gene_Combination_using_Fuzzy_Logic_IJARIIE1137_volume_1_11_page_43_48.pdf (Accessed : ).
Harvard Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi. (2015) 'Classification of Gene Expression Data by Gene Combination using Fuzzy Logic', International Journal Of Advance Research And Innovative Ideas In Education, 1(2), pp. 43-48IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Classification_of_Gene_Expression_Data_by_Gene_Combination_using_Fuzzy_Logic_IJARIIE1137_volume_1_11_page_43_48.pdf (Accessed : )
IEEE Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi, "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic," International Journal Of Advance Research And Innovative Ideas In Education, vol. 1, no. 2, pp. 43-48, May-Jun 2015. [Online]. Available: http://ijariie.com/AdminUploadPdf/Classification_of_Gene_Expression_Data_by_Gene_Combination_using_Fuzzy_Logic_IJARIIE1137_volume_1_11_page_43_48.pdf [Accessed : ].
Turabian Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi. "Classification of Gene Expression Data by Gene Combination using Fuzzy Logic." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 1 number 2 ().
Vancouver Daxa Ghadiya, Prof. Ankit Kharwar , and Prof. Monali Gandhi. Classification of Gene Expression Data by Gene Combination using Fuzzy Logic. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2015 [Cited : ]; 1(2) : 43-48. Available from: http://ijariie.com/AdminUploadPdf/Classification_of_Gene_Expression_Data_by_Gene_Combination_using_Fuzzy_Logic_IJARIIE1137_volume_1_11_page_43_48.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Driver Drowsiness Detection System By Measuring EAR and MARComputer Engineering Zoya Fatema Khan Download
Cloud-Based File Storage and Sharing Web ApplicationComputer Science & EngineeringMohammed Aadil Download
STUDYNEST: ONLINE LEARNING PLATFORM WITH AI RECOMMENDATION SYSTEMComputer Science and Engineering Urvashi Suresh Waghmare Download
Leveraging AWS for Developing and Hosting a Dynamic Food Ordering Web ApplicationComputer EngineeringAfshin Khanam Download
Edge-to-Cloud Synergy: An Autoencoder-GAN Framework for Anomaly Detection in Healthcare Records, Financial Statements, and Secure Cloud StorageInformation TechnologyKarthik Kushala Download
E-Commerce Fraud Detection Based on Machine Learning TechniquesComputer Science EngneeringVikram Ankush Ade Download
Detection of Phishing Website Using Gradient Boosting AlgorithmComputer Science and EngineeringYAWALKAR PRASAD PRAMOD Download
Property Dealing WebComputer Science EngineeringYash Chaudhari Download
Reinforcement Learning for the Evolution of Antimicrobial Nano formulationsmachine learningMadhusudan Download
SecuraVault: A secured blockchain based cloud storage systemComputer Engineering Anshika Jaiswal Download
Autoimmune Disease Detection in women Using Machine Learning Approachcomputer science EngineeringJ. L. V. S. Download
Medicine Overdose Detection System Using Machine LearningComputer Science EngineeringDr.Somashekhar B M Download
Home Price Prediction Using Machine LearningComputer Science & Engineering G Tushar Download
Heat diseases prediction using machine learningComputer EngineeringProf. Meghashree M B Download
GRIDSHIELD AIComputer EngineeringDr. Archana B Download
12