Predicting the predisposition to colorectal cancer based on SNP profiles of immune phenotypes using supervised learning models

Ali Cakmak*, Huzeyfe Ayaz, Soykan Arıkan, Ali R. Ibrahimzada, Şeyda Demirkol, Dilara Sönmez, Mehmet T. Hakan, Saime T. Sürmen, Cem Horozoğlu, Mehmet B. Doğan, Özlem Küçükhüseyin, Canan Cacına, Bayram Kıran, Ümit Zeybek, Mehmet Baysan, İlhan Yaylım

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2 Atıf (Scopus)

Özet

This study explores the machine learning-based assessment of predisposition to colorectal cancer based on single nucleotide polymorphisms (SNP). Such a computational approach may be used as a risk indicator and an auxiliary diagnosis method that complements the traditional methods such as biopsy and CT scan. Moreover, it may be used to develop a low-cost screening test for the early detection of colorectal cancers to improve public health. We employ several supervised classification algorithms. Besides, we apply data imputation to fill in the missing genotype values. The employed dataset includes SNPs observed in particular colorectal cancer-associated genomic loci that are located within DNA regions of 11 selected genes obtained from 115 individuals. We make the following observations: (i) random forest-based classifier using one-hot encoding and K-nearest neighbor (KNN)-based imputation performs the best among the studied classifiers with an F1 score of 89% and area under the curve (AUC) score of 0.96. (ii) One-hot encoding together with K-nearest neighbor-based data imputation increases the F1 scores by around 26% in comparison to the baseline approach which does not employ them. (iii) The proposed model outperforms a commonly employed state-of-the-art approach, ColonFlag, under all evaluated settings by up to 24% in terms of the AUC score. Based on the high accuracy of the constructed predictive models, the studied 11 genes may be considered a gene panel candidate for colon cancer risk screening. Graphical Abstract: [Figure not available: see fulltext.].

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)243-258
Sayfa sayısı16
DergiMedical and Biological Engineering and Computing
Hacim61
Basın numarası1
DOI'lar
Yayın durumuYayınlandı - Oca 2023

Bibliyografik not

Publisher Copyright:
© 2022, International Federation for Medical and Biological Engineering.

Finansman

This study was funded by the Scientific Research Projects Coordination Unit of Istanbul University (BYP-2018–28139 and TYO-2009–33675).

FinansörlerFinansör numarası
Istanbul ÜniversitesiTYO-2009–33675, BYP-2018–28139

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