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

Sami Akbulut; Zeynep Küçükakçalı; Cemil Çolak

doi:10.17826/cumj.1128653

EN TR

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

Abstract

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.

Keywords

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 Kelimeler

Kolorektal kanser , Genomik , Makine öğrenimi , XGBoost modeli

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Klinik Tıp Bilimleri

Bölüm

Araştırma Makalesi

Yazarlar

Sami Akbulut ^*
0000-0002-6864-7711
Türkiye

Zeynep Küçükakçalı
0000-0001-7956-9272
Türkiye

Cemil Çolak
0000-0001-5406-098X
Türkiye

Yayımlanma Tarihi

30 Eylül 2022

Gönderilme Tarihi

10 Haziran 2022

Kabul Tarihi

25 Temmuz 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 47 Sayı: 3

DOI

https://doi.org/10.17826/cumj.1128653

IZ

https://izlik.org/JA27UT68CA

MLA

Akbulut, Sami, vd. “Classification of colorectal cancer based on gene sequencing data with XGBoost model: An application of public health informatics”. Cukurova Medical Journal, c. 47, sy 3, Eylül 2022, ss. 1179-86, doi:10.17826/cumj.1128653.

Cited By

A precise neural network approach using residual network and minority oversampling method to diagnose the correlation between colorectal cancer and food group

Results in Engineering

https://doi.org/10.1016/j.rineng.2025.106909

Bu eser Creative Commons Atıf-Gayriticari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

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

Abstract

Keywords

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

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

A precise neural network approach using residual network and minority oversampling method to diagnose the correlation between colorectal cancer and food group