Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Training of ANFIS Network by Genetic Algorithm for Diagnosis of Leukemia Cancer Subtypes Using Gene Expression Profile
Date
2017-05-12
Author
Arslan, Mustafa Turan
Haznedar, Bülent
Kalınlı, Adem
Metadata
Show full item record
Item Usage Stats
304
views
0
downloads
Cite This
In this study, subtypes of Leukemia cancer has classified by using microarray gene expression profiles. An approach is proposed to train Adaptive Neuro Fuzzy Inference System (ANFIS) network by using a population-based Genetic Algorithm (GA) to classify this cancer data. The classification success of the proposed model has compared with the successes of Backpropagation (BP)-ANFIS and Hybrid-ANFIS, which are derivative based ANFIS models. According to obtained results, GA-ANFIS model has performed very well on leukemia cancer, with 90.91% in the classification study. For the same data, BPANFIS and Hybrid-ANFIS models have performed poorly with 63.63% and 59.09%, respectively.
Subject Keywords
Microarray gene expression profile
,
ANFIS
,
Genetic Algorithm
,
Classification
URI
https://hdl.handle.net/11511/73791
https://www.icatsconf.org/uploads/files2/icat17-proceeding-v2-1.pdf
Conference Name
5th International Conference on Advanced Technology & Sciences (ICAT'17), (May 09-12)
Collections
Other, Conference / Seminar
Suggestions
OpenMETU
Core
A Comparative Study of Statistical and Artificial Intelligence based Classification Algorithms on Central Nervous System Cancer Microarray Gene Expression Data
Arslan, Mustafa Turan; Kalınlı, Adem (2016-09-03)
A variety of methods are used in order to classify cancer gene expression profiles based on microarray data. Especially, statistical methods such as Support Vector Machines (SVM), Decision Trees (DT) and Bayes are widely preferred to classify on microarray cancer data. However, the statistical methods can often be inadequate to solve problems which are based on particularly large-scale data such as DNA microarray data. Therefore, artificial intelligence-based methods have been used to classify on microarray...
Karaciğer mikrodizi kanser verisinin sınıflandırılması için genetik algoritma kullanarak ANFIS’in eğitilmesi
Haznedar, Bülent; Arslan, Mustafa Turan; Kalınlı, Adem (2017-01-01)
Sınıflandırma, verilerin analiz edilmesi için önemli bir veri madenciliği tekniği olup tıp, genetik ve biyomedikal mühendisliği başta olmak üzere birçok alanda kullanılmaktadır. Özellikle tıp alanında DNA mikrodizi gen ekspresyon verilerini sınıflandırmaya yönelik yapılan çalışmalarda artış görülmektedir. Ancak, mikrodizi gen ekspresyon (ifade) verilerinde bulunan gen sayılarının çokluğu ve bu veriler arasında doğrusal olmayan bağıntılar bulunması gibi problemlerden dolayı geleneksel sınıflandırma algor...
Identification of gene mutations involved in drug resistance in liver cancer using RNA-SEQ data analysis
Shojaei, Mona; Atalay, Rengül; Acar, Aybar Can; Department of Bioinformatics (2016)
A significant concern in cancer research is the detection of cancer associated somatic mutations. Liver cancer is the 5th most common and 2nd deadliest cancer in the world. Several somatic mutations were previously reported in liver cancer but their relations to chemotherapeutic response was not studied in detail. In this study, the relationship between mutation status and drug treatment response of well-differentiated Huh7 and poorly-differentiated Mahlavu liver cancer cells were analyzed. The RNA-Seq data...
DETERMINATION OF THE RELATIONSHIP BETWEEN DOXORUBICIN RESISTANCE AND WNT SIGNALING PATHWAY IN HELA AND K562 CELL LINES
Mutlu, Pelin; Pourianazar, Negar Taghavi; Yucel, Meral; Gündüz, Ufuk (2018-01-01)
Activation of the Wnt signaling in some types of cancer and its relation with chemotherapy resistance is a very interesting issue that has been emphasized in recent years. Although, it is known that increase in the activity of beta-catenin is important in blast transformation and drug resistance, the underlying mechanisms are still unclear. In this study, changes in the expression levels of 186 genes that are thought to be important in drug resistance and Wnt signaling pathways were determined by using qPCR...
Evaluation of methylation profiles of an epidermal growth factor receptor gene in a head and neck squamous cell carcinoma patient group
Mutlu, M.; Mutlu, Pelin; Azarkan, S.; Baylr, Ö.; Öcal, B.; Saylam, G.; KORKMAZ, MEHMET HAKAN (2021-03-23)
Upregulation of the epidermal growth factor receptor (EGFR) gene has shown an important impact on the development of head and neck cancers due to its important regulation role on multiple cell signaling pathways. The aim of this study was to investigate the methylation pattern of the promoter region of the EGFR gene between head and neck squamous cell carcinoma (HNSCC) patients and a control group. Forty-seven unrelated HNSCC patients, clinically diagnosed at the Department of Otorhinolaryngology, Dlşkapl Y...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. T. Arslan, B. Haznedar, and A. Kalınlı, “Training of ANFIS Network by Genetic Algorithm for Diagnosis of Leukemia Cancer Subtypes Using Gene Expression Profile,” İstanbul, Türkiye, 2017, p. 19, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73791.
