Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics

ÇOLAK, Cemil; KÜÇÜKAKÇALI, ZEYNEP; Akbulut, Sami

doi:10.17826/cumj.1128653

Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics

AHMET SAMİ AKBULUT, (İnönü Üniversitesi, Tıp Fakültesi, Cerrahi Anabilim Dalı, Malatya, Türkiye)

Zeynep TUNÇ, (İnönü Üniversitesi, Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi Anabilim Dalı, Malatya, Türkiye)

Cemil ÇOLAK (İnönü Üniversitesi, Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi Anabilim Dalı, Malatya, Türkiye)

Cukurova Medical Journal

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Yıl: 2022 Cilt: 47 Sayı: 3 Sayfa Aralığı: 1179 - 1186 Metin Dili: İngilizce DOI: 10.17826/cumj.1128653 İndeks Tarihi: 20-09-2022

Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics

Öz:

Purpose: This study aims to classify open-access colorectal cancer gene data and identify essential genes with the XGBoost method, a machine learning method. Materials and Methods: The open-access colorectal cancer gene dataset was used in the study. The dataset included gene sequencing results of 10 mucosae from healthy controls and the colonic mucosa of 12 patients with colorectal cancer. XGboost, one of the machine learning methods, was used to classify the disease. Accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, and negative predictive value performance metrics were evaluated for model performance. Results: According to the variable selection method, 17 genes were selected, and modeling was performed with these input variables. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score obtained from modeling results were 95.5%, 95.8%, 91.7%, 1%, 1%, and 90.9%, and 95.7%, respectively. According to the variable impotance acquired from the XGboost technique results, the CYR61, NR4A, FOSB, and NR4A2 genes can be employed as biomarkers for colorectal cancer. Conclusion: As a consequence of this research, genes that may be linked to colorectal cancer and genetic biomarkers for the illness were identified. In the future, the detected genes' reliability can be verified, therapeutic procedures can be established based on these genes, and their usefulness in clinical practice may be documented.

Anahtar Kelime: Colorectal cancer, Genomics, Machine learning, XGBoost model

XGBoost modeli ile gen dizileme verilerine dayalı kolorektal kanserin sınıflandırılması: Bir halk sağlığı bilişimi uygulaması

Öz:

Amaç: Bu çalışma, bir makine öğrenmesi yöntemi olan XGBoost yöntemi ile açık erişimli kolorektal kanser gen verilerini sınıflandırmayı ve temel genleri tanımlamayı amaçlamaktadır. Gereç ve Yöntem: Çalışmada açık erişimli kolorektal kanser gen veri seti kullanıldı. Veri seti, sağlıklı kontrollerden 10 mukozanın ve kolorektal kanserli 12 hastanın kolon mukozasının gen dizileme sonuçlarını içeriyordu. Hastalığı sınıflandırmak için makine öğrenmesi yöntemlerinden biri olan XGboost kullanıldı. Model performansı için doğruluk, dengelenmiş doğruluk, duyarlılık, seçicilik, pozitif tahmin değeri ve negatif tahmin değeri performans metrikleri değerlendirildi. Bulgular: Değişken seçim yöntemine göre 17 gen seçilmiş ve bu girdi değişkenleri ile modelleme yapılmıştır. Modelleme sonuçlarından elde edilen doğruluk, dengeli doğruluk, duyarlılık, özgüllük, pozitif tahmin değeri, negatif tahmin değeri ve F1 puanı sırasıyla %95.5, %95.8, %91.7, %1, %1 ve %90.9 ve %95.7 idi. XGboost tekniği sonucundan elde edilen değişken önemliliklerine göre, CYR61, NR4A, FOSB ve NR4A2 genleri kolorektal kanser için biyolojik belirteçler olarak kullanılabilir. Sonuç: Bu araştırma sonucunda kolorektal kanserle bağlantılı olabilecek genlerin yanı sıra hastalığa yönelik genetik biyobelirteçler de belirlendi. Gelecekte, tespit edilen genlerin güvenilirliği doğrulanabilir, bu genlere dayalı olarak terapötik prosedürler oluşturulabilir ve klinik pratikteki yararları belgelenebilir.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	Akbulut S, KÜÇÜKAKÇALI Z, ÇOLAK C (2022). Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. , 1179 - 1186. 10.17826/cumj.1128653
Chicago	Akbulut Sami,KÜÇÜKAKÇALI ZEYNEP,ÇOLAK Cemil Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. (2022): 1179 - 1186. 10.17826/cumj.1128653
MLA	Akbulut Sami,KÜÇÜKAKÇALI ZEYNEP,ÇOLAK Cemil Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. , 2022, ss.1179 - 1186. 10.17826/cumj.1128653
AMA	Akbulut S,KÜÇÜKAKÇALI Z,ÇOLAK C Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. . 2022; 1179 - 1186. 10.17826/cumj.1128653
Vancouver	Akbulut S,KÜÇÜKAKÇALI Z,ÇOLAK C Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. . 2022; 1179 - 1186. 10.17826/cumj.1128653
IEEE	Akbulut S,KÜÇÜKAKÇALI Z,ÇOLAK C "Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics." , ss.1179 - 1186, 2022. 10.17826/cumj.1128653
ISNAD	Akbulut, Sami vd. "Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics". (2022), 1179-1186. https://doi.org/10.17826/cumj.1128653

APA	Akbulut S, KÜÇÜKAKÇALI Z, ÇOLAK C (2022). Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. Cukurova Medical Journal, 47(3), 1179 - 1186. 10.17826/cumj.1128653
Chicago	Akbulut Sami,KÜÇÜKAKÇALI ZEYNEP,ÇOLAK Cemil Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. Cukurova Medical Journal 47, no.3 (2022): 1179 - 1186. 10.17826/cumj.1128653
MLA	Akbulut Sami,KÜÇÜKAKÇALI ZEYNEP,ÇOLAK Cemil Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. Cukurova Medical Journal, vol.47, no.3, 2022, ss.1179 - 1186. 10.17826/cumj.1128653
AMA	Akbulut S,KÜÇÜKAKÇALI Z,ÇOLAK C Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. Cukurova Medical Journal. 2022; 47(3): 1179 - 1186. 10.17826/cumj.1128653
Vancouver	Akbulut S,KÜÇÜKAKÇALI Z,ÇOLAK C Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics. Cukurova Medical Journal. 2022; 47(3): 1179 - 1186. 10.17826/cumj.1128653
IEEE	Akbulut S,KÜÇÜKAKÇALI Z,ÇOLAK C "Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics." Cukurova Medical Journal, 47, ss.1179 - 1186, 2022. 10.17826/cumj.1128653
ISNAD	Akbulut, Sami vd. "Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics". Cukurova Medical Journal 47/3 (2022), 1179-1186. https://doi.org/10.17826/cumj.1128653