Araştırma Makalesi
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Mikrodizi Veri Kümesi Üzerinde Doğadan İlham Alan Optimizasyon ile Birleştirilen Uyarlanabilir Ağ Tabanlı Bulanık Çıkarım Sistemi Kullanılarak T-ALL, B-ALL ve T-LL Malignitelerinin Sınıflandırılması

Yıl 2023, Cilt: 23 Sayı: 4, 941 - 954, 31.08.2023
https://doi.org/10.35414/akufemubid.1259929

Öz

Lösemi farklı karakteristik bulgular gösteren kanser oluşumudur. Hastalığın vücut içerisinde ilerleme biçimine göre akut ya da kronik olarak isimlendirilir. Akut lösemiler, kemik iliğinde kontrolsüz çoğalan ve ardından kana ve dokulara geçen blast hücrelerinin varlığı ile karakterize edilir. Blast hücrelerinin alt türlerine ilişkin immünfenotipik değerlendirme sürecinde T/B ya da non T/B hücre sınıfının belirlenmesi önemlidir. Çünkü, B ve T hücre serisinden meydana gelen B-ALL, T-ALL ve T-LL alt türlerinin teşhis ve tedavi süreçleri farklıdır. Bu nedenle doğru tanı hayatidir. Bu çalışmada, mikrodizi veri kümeleri vasıtasıyla T-ALL, B-ALL ve T-LL alt türlerinin doğru tespiti için moleküler tanı sağlanmıştır. Fakat mikrodizi veri kümeleri, çok boyutlu bir yapıya sahiptir. Çünkü hastalıkla ilişkili bilgilerin yanı sıra hastalıkla ilişkisiz bilgiler de barındırmaktadır. Bu durum modelin eğitim durumunu ve hesaplama maliyetini de etkilemektedir. Bunun için çalışmanın ilk aşamasında balina optimizasyon algoritması kullanılmıştır. Böylece ilişkili genler veri setinden seçilmiştir. İkinci olarak seçilen potansiyel genler ANFIS yapısına girdi olarak verilmiştir. Ardından çıkarım gücünü iyileştirmek için ABC ve PSO optimizasyon algoritmaları ile ANFIS yapısının üyelik fonksiyonuna ilişkin parametre optimizasyonu sağlanmıştır. Son olarak her bir örnek için ANFIS, ANFIS+ABC, ANFIS+PSO yöntemlerinden elde edilen tahminler, lojistik regresyon algoritması kullanılarak sınıflandırılmış ve %86,6 doğruluk oranı elde edilmiştir.

Kaynakça

  • Yöntem, A. and Bayram I., 2018. Çocukluk Çaginda Akut Lenfoblastik Lösemi. Archives Medical Review Journal, 27(4), 483–499.
  • Tecimer, T., 2001. Prekürsör B ve T Lenfoblastik Lösemi / Lenfoblastik Lenfoma Patolojisi. Türk Hematoloji Dernegi, Klinisyen-Patolog Ortak Lenfoma Kursu. 24–27.
  • Shiraz, P., Jehangir, W. and Agrawal, V., 2021. T-cell acute lymphoblastic leukemia—current concepts in molecular biology and management. Biomedicines. 9(11), 1–19.
  • Hoelzer, D. and Gökbuget, N., 2009. T-cell lymphoblastic lymphoma and T-cell acute lymphoblastic leukemia: a separate entity?. Clinical Lymphoma & Myeloma & Leukemia Supplement, 9, S214–S221.
  • Raetz, E.A. and Teachey, D.T., 2016. T-cell acute lymphoblastic leukemia. Pediatric Hematologic Malignancies, 2016(2), 580–588.
  • Hambali, M.A., Oladele, T.O. and Adewole, K.S., 2020. Microarray cancer feature selection: Review, challenges and research directions. International Journal of Cognitive Computing in Engineering, 1, 78–97.
  • Karaboga, D. and Kaya, E., 2016. An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Applied Soft Computing Journal, 49, 423–436.
  • Mishra, P. and Bhoi, N., 2021. Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique. Biocybernetics and Biomedical Engineering, 41(3), 916–932.
  • Sayed, S., Nassef, M., Badr, A. and Farag, I., 2019. A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets. Expert Systems with Applications, 121, 233–243.
  • S., S. and G., H.G, 2020. A novel distance measure for microarray dataset using entropy. Materials Today: Proceedings.
  • Arun Kumar, C., P.S., M. and Ramakrishnan, S., 2017. A Comparative Performance Evaluation of Supervised Feature Selection Algorithms on Microarray Datasets. Procedia Computer Science, 115, 209–217.
  • Abd-Elnaby, M., Alfonse, M. and Roushdy, M., 2021. Classification of breast cancer using microarray gene expression data: A survey. Journal of Biomedical Informatics, 117, 1-9.
  • Saeid, M.M., Nossair, Z.B., Saleh, M.A., 2020. A microarray cancer classification technique based on discrete wavelet transform for data reduction and genetic algorithm for feature selection. Proceedings of the Fourth International Conference on Trends in Electronics and Informatics (ICOEI 2020). https://file.biolab.si/biolab/supp/bi-cancer/projections, (2022).
  • Begum, S., Sarkar, R., Chakraborty, D., Sen, S. and Maulik, U., 2021. Application of active learning in DNA microarray data for cancerous gene identification, Expert Systems with Applications. 177, 1-8.
  • Wang, X., and Simon, R., 2011. Microarray-based cancer prediction using single genes. BMC Bioinformatics. 12, 1-9.
  • Alshamlan, H.M., Badr, G.H. and Alohali, Y.A., 2015. Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification. Computational Biology and Chemistry. 56, 49–60.
  • Panda, M., 2020. Elephant search optimization combined with deep neural network for microarray data analysis. Journal of King Saud University - Computer and Information Sciences. 32, 940–948.
  • Khorshed, T., Moustafa, M.N. and Rafea, A., 2020. Learning Visualizing Genomic Signatures of Cancer Tumors using Deep Neural Networks. Proceedings of the International Joint Conference on Neural Networks.
  • Xu, R. Anagnostopoulos, G.C. and Wunsch, D.C., 2007. Multiclass cancer classification using semisupervised ellipsoid ARTMAP and particle swarm optimization with gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(1), 65–77.
  • Ocampo-Vega, R., Sanchez-Ante, G., De Luna, M.A., Vega, R., Falcón-Morales, L.E. and Sossa H., 2016. Improving pattern classification of DNA microarray data by using PCA and Logistic Regression. Intelligent Data Analysis, 20, S53–S67.
  • Li, J., Liang, K., and Song, X., 2022. Logistic regression with adaptive sparse group lasso penalty and its application in acute leukemia diagnosis. Computers in Biology and Medicine, 141, 1-10.
  • Canayaz, M. and Demir, M. 2017. Balina Optimizasyon Algoritması ve Yapay Sinir Ağı ile Öznitelik Seçimi. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP).
  • Vafaei, A. and Aliehyaei, M.A., 2020. Optimization of micro gas turbine by economic, exergy and environment analysis using genetic, bee colony and searching algorithms. Journal of Thermal Engineering, 6(1), 117–140.
  • Doğan, C., 2019. Balina Optimizasyon Algoritması ve Gri Kurt Optimizasyonu Algoritmaları Kullanılarak Yeni Hibrit Optimizasyon Algoritmalarının Geliştirilmesi, Yüksek Lisans Tezi, Erciyes Üniversitesi, Kayseri, 55.
  • Rana, N., Latiff, M.S.A, Abdulhamid, S.M, and Chiroma, H., 2020. Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Computing and Applications, 32(20), 16245–16277, Mirjalili, S. and Lewis, A., 2016. The Whale Optimization Algorithm, Advances in Engineering Software, 95, 51–67.
  • Mahdevari, S. and Khodabakhshi, M.B., 2021. A hybrid PSO-ANFIS model for predicting unstable zones in underground roadways. Tunnelling and Underground Space Technology, 117, 1-18.
  • Başlıgil, H., 2005. Bulanık AHP ile Yazılım Seçimi, Mühendislik ve Fen Bilimleri Dergisi, 3, 24–33.
  • Karaboga, D. and Kaya, E, 2020. Estimation of number of foreign visitors with ANFIS by using ABC algorithm. Soft Computing, 24, 7579–7591.
  • Chen, Y. and Zhao, Y., 2008. A novel ensemble of classifiers for microarray data classification. Applied Soft Computing Journal, 8, 1664–1669.
  • Houssein, E.H., Gad, A.G., Hussain, K. and Suganthan, P.N., 2021. Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application, Swarm and Evolutionary Computation, 63, 1-39.
  • Zhao, Z.Q., Zheng, P., Xu, S.T., and Wu, X., 2019. Object Detection with Deep Learning: A Review, IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212-3232.
  • El Mrabet, M.A., El Makkaoui, K. and Faize, A., 2021. Supervised Machine Learning: A Survey, Proceedings-4th International Conference on Advanced Communication Technologies and Networking, CommNet 2021.
  • Guerrero, M.C., Parada, J.S. and Espitia, H.E., 2021. EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon, e07258, 1-19
  • Zamfirache, I.A., Precup, R.E., Roman, R.C., and Petriu, E.M., 2022. Policy Iteration Reinforcement Learning-based control using a Grey Wolf Optimizer algorithm. Information Sciences, 585, 162–175.
  • Precup, R.E., Bojan-Dragos, C.A., Hedrea, E.L., Roman, R.C. and Petriu, E.M., 2021. Evolving Fuzzy Models of Shape Memory Alloy Wire Actuators. Romanian Journal of Information Science and Technology, 24(4), 353–365.
  • Akalın, F. and Yumuşak, N., 2023. Lösemi hastalığının temel türlerinden ALL ve KML malignitelerinin graf sinir ağları ve bulanık mantık algoritması ile sınıflandırılması. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(2), 707–719, 2023.
  • Öğütcü, S., İnal, M., Çelikhasi, C., Yildiz, U., Doğan, N.Ö. and Pekdemir, M., 2022. Early Detection of Mortality in COVID-19 Patients Through Laboratory Findings with Factor Analysis and Artificial Neural Networks, Romanian Journal of Information Science and Technology, 25(3–4), 290–302.
  • Peng, S., Xu, Q., Ling, X.B., Peng, X., Du, W. and Chen, L., 2003. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Letters, 555(2), 358–362.
  • Xu, R., Anagnostopoulos, G.C. and Wunsch, D.C., 2007. Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(1), 65–77.
  • Chakraborty, D. and Maulik, U., 2014. Identifying Cancer Biomarkers from Microarray Data Using Feature Selection and Semisupervised Learning. IEEE Journal of Translational Engineering in Health and Medicine, 2, 1–11.
  • Dagliyan, O., Yuksektepe, F.U., Kavakli, I.H. and Turkay, M., 2011. Optimization based tumor classification from microarray gene expression data. PLoS One, 6(2), 1-10.
  • Kar, S., Sharma, K.D. and Maitra, M., 2015. Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique. Expert Systems with Applications, 42(1), 612–627.

Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset

Yıl 2023, Cilt: 23 Sayı: 4, 941 - 954, 31.08.2023
https://doi.org/10.35414/akufemubid.1259929

Öz

Leukemia is the formation of cancer with different characteristic findings. According to the progress type of disease in the body is called acute or chronic. Acute leukemias are characterized by the presence of blast cells that proliferate uncontrollably in the bone marrow and then go into the blood and tissues. Determination of T/B or non T/B cell class is important in the immunophenotypic evaluation related to subtypes of blast cells. Because the diagnosis and treatment processes of B-ALL, T-ALL and T-LL subtypes, which are composed of B and T cell lines, are different. Therefore, correct diagnosis is vital. In this study, the molecular diagnosis was provided for the accurate detection of T-ALL, B-ALL and T-LL subtypes through microarray datasets. But, microarray datasets have a multidimensional structure. Because it contains information related to the disease as well as information not related to the disease. This situation also affects the training situation and computational cost of the model. For this, the whale optimization algorithm was used in the first stage of the study. Thus, related genes were selected from the data set. Secondly, the selected potential genes were given as input to the ANFIS structure. Then, in order to improve the inference power, parameter optimization related to the membership function of the ANFIS structure was provided with ABC and PSO optimization algorithms. Finally, the predictions obtained from the ANFIS, ANFIS+ABC, and ANFIS+PSO methods for each sample were classified using the logistic regression algorithm and, an accuracy rate of 86.6% was obtained.

Kaynakça

  • Yöntem, A. and Bayram I., 2018. Çocukluk Çaginda Akut Lenfoblastik Lösemi. Archives Medical Review Journal, 27(4), 483–499.
  • Tecimer, T., 2001. Prekürsör B ve T Lenfoblastik Lösemi / Lenfoblastik Lenfoma Patolojisi. Türk Hematoloji Dernegi, Klinisyen-Patolog Ortak Lenfoma Kursu. 24–27.
  • Shiraz, P., Jehangir, W. and Agrawal, V., 2021. T-cell acute lymphoblastic leukemia—current concepts in molecular biology and management. Biomedicines. 9(11), 1–19.
  • Hoelzer, D. and Gökbuget, N., 2009. T-cell lymphoblastic lymphoma and T-cell acute lymphoblastic leukemia: a separate entity?. Clinical Lymphoma & Myeloma & Leukemia Supplement, 9, S214–S221.
  • Raetz, E.A. and Teachey, D.T., 2016. T-cell acute lymphoblastic leukemia. Pediatric Hematologic Malignancies, 2016(2), 580–588.
  • Hambali, M.A., Oladele, T.O. and Adewole, K.S., 2020. Microarray cancer feature selection: Review, challenges and research directions. International Journal of Cognitive Computing in Engineering, 1, 78–97.
  • Karaboga, D. and Kaya, E., 2016. An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Applied Soft Computing Journal, 49, 423–436.
  • Mishra, P. and Bhoi, N., 2021. Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique. Biocybernetics and Biomedical Engineering, 41(3), 916–932.
  • Sayed, S., Nassef, M., Badr, A. and Farag, I., 2019. A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets. Expert Systems with Applications, 121, 233–243.
  • S., S. and G., H.G, 2020. A novel distance measure for microarray dataset using entropy. Materials Today: Proceedings.
  • Arun Kumar, C., P.S., M. and Ramakrishnan, S., 2017. A Comparative Performance Evaluation of Supervised Feature Selection Algorithms on Microarray Datasets. Procedia Computer Science, 115, 209–217.
  • Abd-Elnaby, M., Alfonse, M. and Roushdy, M., 2021. Classification of breast cancer using microarray gene expression data: A survey. Journal of Biomedical Informatics, 117, 1-9.
  • Saeid, M.M., Nossair, Z.B., Saleh, M.A., 2020. A microarray cancer classification technique based on discrete wavelet transform for data reduction and genetic algorithm for feature selection. Proceedings of the Fourth International Conference on Trends in Electronics and Informatics (ICOEI 2020). https://file.biolab.si/biolab/supp/bi-cancer/projections, (2022).
  • Begum, S., Sarkar, R., Chakraborty, D., Sen, S. and Maulik, U., 2021. Application of active learning in DNA microarray data for cancerous gene identification, Expert Systems with Applications. 177, 1-8.
  • Wang, X., and Simon, R., 2011. Microarray-based cancer prediction using single genes. BMC Bioinformatics. 12, 1-9.
  • Alshamlan, H.M., Badr, G.H. and Alohali, Y.A., 2015. Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification. Computational Biology and Chemistry. 56, 49–60.
  • Panda, M., 2020. Elephant search optimization combined with deep neural network for microarray data analysis. Journal of King Saud University - Computer and Information Sciences. 32, 940–948.
  • Khorshed, T., Moustafa, M.N. and Rafea, A., 2020. Learning Visualizing Genomic Signatures of Cancer Tumors using Deep Neural Networks. Proceedings of the International Joint Conference on Neural Networks.
  • Xu, R. Anagnostopoulos, G.C. and Wunsch, D.C., 2007. Multiclass cancer classification using semisupervised ellipsoid ARTMAP and particle swarm optimization with gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(1), 65–77.
  • Ocampo-Vega, R., Sanchez-Ante, G., De Luna, M.A., Vega, R., Falcón-Morales, L.E. and Sossa H., 2016. Improving pattern classification of DNA microarray data by using PCA and Logistic Regression. Intelligent Data Analysis, 20, S53–S67.
  • Li, J., Liang, K., and Song, X., 2022. Logistic regression with adaptive sparse group lasso penalty and its application in acute leukemia diagnosis. Computers in Biology and Medicine, 141, 1-10.
  • Canayaz, M. and Demir, M. 2017. Balina Optimizasyon Algoritması ve Yapay Sinir Ağı ile Öznitelik Seçimi. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP).
  • Vafaei, A. and Aliehyaei, M.A., 2020. Optimization of micro gas turbine by economic, exergy and environment analysis using genetic, bee colony and searching algorithms. Journal of Thermal Engineering, 6(1), 117–140.
  • Doğan, C., 2019. Balina Optimizasyon Algoritması ve Gri Kurt Optimizasyonu Algoritmaları Kullanılarak Yeni Hibrit Optimizasyon Algoritmalarının Geliştirilmesi, Yüksek Lisans Tezi, Erciyes Üniversitesi, Kayseri, 55.
  • Rana, N., Latiff, M.S.A, Abdulhamid, S.M, and Chiroma, H., 2020. Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Computing and Applications, 32(20), 16245–16277, Mirjalili, S. and Lewis, A., 2016. The Whale Optimization Algorithm, Advances in Engineering Software, 95, 51–67.
  • Mahdevari, S. and Khodabakhshi, M.B., 2021. A hybrid PSO-ANFIS model for predicting unstable zones in underground roadways. Tunnelling and Underground Space Technology, 117, 1-18.
  • Başlıgil, H., 2005. Bulanık AHP ile Yazılım Seçimi, Mühendislik ve Fen Bilimleri Dergisi, 3, 24–33.
  • Karaboga, D. and Kaya, E, 2020. Estimation of number of foreign visitors with ANFIS by using ABC algorithm. Soft Computing, 24, 7579–7591.
  • Chen, Y. and Zhao, Y., 2008. A novel ensemble of classifiers for microarray data classification. Applied Soft Computing Journal, 8, 1664–1669.
  • Houssein, E.H., Gad, A.G., Hussain, K. and Suganthan, P.N., 2021. Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application, Swarm and Evolutionary Computation, 63, 1-39.
  • Zhao, Z.Q., Zheng, P., Xu, S.T., and Wu, X., 2019. Object Detection with Deep Learning: A Review, IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212-3232.
  • El Mrabet, M.A., El Makkaoui, K. and Faize, A., 2021. Supervised Machine Learning: A Survey, Proceedings-4th International Conference on Advanced Communication Technologies and Networking, CommNet 2021.
  • Guerrero, M.C., Parada, J.S. and Espitia, H.E., 2021. EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon, e07258, 1-19
  • Zamfirache, I.A., Precup, R.E., Roman, R.C., and Petriu, E.M., 2022. Policy Iteration Reinforcement Learning-based control using a Grey Wolf Optimizer algorithm. Information Sciences, 585, 162–175.
  • Precup, R.E., Bojan-Dragos, C.A., Hedrea, E.L., Roman, R.C. and Petriu, E.M., 2021. Evolving Fuzzy Models of Shape Memory Alloy Wire Actuators. Romanian Journal of Information Science and Technology, 24(4), 353–365.
  • Akalın, F. and Yumuşak, N., 2023. Lösemi hastalığının temel türlerinden ALL ve KML malignitelerinin graf sinir ağları ve bulanık mantık algoritması ile sınıflandırılması. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(2), 707–719, 2023.
  • Öğütcü, S., İnal, M., Çelikhasi, C., Yildiz, U., Doğan, N.Ö. and Pekdemir, M., 2022. Early Detection of Mortality in COVID-19 Patients Through Laboratory Findings with Factor Analysis and Artificial Neural Networks, Romanian Journal of Information Science and Technology, 25(3–4), 290–302.
  • Peng, S., Xu, Q., Ling, X.B., Peng, X., Du, W. and Chen, L., 2003. Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Letters, 555(2), 358–362.
  • Xu, R., Anagnostopoulos, G.C. and Wunsch, D.C., 2007. Multi-class cancer classification by semi-supervised ellipsoid ARTMAP with gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(1), 65–77.
  • Chakraborty, D. and Maulik, U., 2014. Identifying Cancer Biomarkers from Microarray Data Using Feature Selection and Semisupervised Learning. IEEE Journal of Translational Engineering in Health and Medicine, 2, 1–11.
  • Dagliyan, O., Yuksektepe, F.U., Kavakli, I.H. and Turkay, M., 2011. Optimization based tumor classification from microarray gene expression data. PLoS One, 6(2), 1-10.
  • Kar, S., Sharma, K.D. and Maitra, M., 2015. Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique. Expert Systems with Applications, 42(1), 612–627.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka, Yazılım Mühendisliği (Diğer)
Bölüm Makaleler
Yazarlar

Fatma Akalın 0000-0001-6670-915X

Nejat Yumuşak 0000-0001-5005-8604

Erken Görünüm Tarihi 29 Ağustos 2023
Yayımlanma Tarihi 31 Ağustos 2023
Gönderilme Tarihi 3 Mart 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 23 Sayı: 4

Kaynak Göster

APA Akalın, F., & Yumuşak, N. (2023). Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 23(4), 941-954. https://doi.org/10.35414/akufemubid.1259929
AMA Akalın F, Yumuşak N. Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. Ağustos 2023;23(4):941-954. doi:10.35414/akufemubid.1259929
Chicago Akalın, Fatma, ve Nejat Yumuşak. “Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined With Nature-Inspired Optimization on Microarray Dataset”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 23, sy. 4 (Ağustos 2023): 941-54. https://doi.org/10.35414/akufemubid.1259929.
EndNote Akalın F, Yumuşak N (01 Ağustos 2023) Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 23 4 941–954.
IEEE F. Akalın ve N. Yumuşak, “Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 23, sy. 4, ss. 941–954, 2023, doi: 10.35414/akufemubid.1259929.
ISNAD Akalın, Fatma - Yumuşak, Nejat. “Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined With Nature-Inspired Optimization on Microarray Dataset”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 23/4 (Ağustos 2023), 941-954. https://doi.org/10.35414/akufemubid.1259929.
JAMA Akalın F, Yumuşak N. Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2023;23:941–954.
MLA Akalın, Fatma ve Nejat Yumuşak. “Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined With Nature-Inspired Optimization on Microarray Dataset”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 23, sy. 4, 2023, ss. 941-54, doi:10.35414/akufemubid.1259929.
Vancouver Akalın F, Yumuşak N. Classification of T-ALL, B-ALL and T-LL Malignancies Using Adaptive Network-Based Fuzzy Inference System Approach Combined with Nature-Inspired Optimization on Microarray Dataset. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2023;23(4):941-54.