GEGE: Predicting Gene Essentiality with Graph Embeddings

Halil İbrahim Kuru; Yasin İlkağan Tepeli; Öznur Taştan

doi:10.29130/dubited.1028387

EN TR

GEGE: Predicting Gene Essentiality with Graph Embeddings

Abstract

A gene is considered essential if its function is indispensable for the viability or reproductive success of a cell or an organism. Distinguishing essential genes from non-essential ones is a fundamental question in genetics, and it is key to understanding the minimal set of functional requirements of an organism. Knowledge of the set of essential genes is also crucial in drug discovery. Several reports in the literature show that the gene location in a protein-protein interaction network is correlated with the target gene’s essentiality. Here, we ask whether the node embeddings of a protein-protein interaction (PPI) network can help predict gene essentiality. Our results on predicting human gene essentiality show that node embeddings alone can achieve up to 88% AUC score, which is better than using topological features to characterize gene properties and other previous work’s results. We also show that, when combined with homology information across species, this performance reaches 89% AUC. Our work shows that node embeddings of a protein in the PPI network capture the network connectivity patterns of the proteins and improve the gene essentiality predictions.

Keywords

Graph representations, Node embeddings, Gene essentiality, Network topological features, Protein-protein interaction network

GEGE: Çizge Gömülümleriyle Gen Esaslılığını Tahmin Etme

Abstract

İşlevi, bir hücrenin veya organizmanın hayatta kalabilmesi veya üreme başarısı için vazgeçilmez olan genler, esaslı genler olarak kabul edilir. Esaslı genleri esaslı olmayanlardan ayırt etmek, bir organizmanın minimum fonksiyonel gereksinimlerinin anlaşılabilmesi için genetikte kilit bir sorudur. Esaslı genler küme bilgisi, ilaç tasarlanmasında da çok önemlidir. Literatürdeki, bir protein-protein etkileşim ağındaki gen konumunun, gen esaslılığı ile ilişkili olduğunu göstermiştir. Burada, bir protein-protein etkileşimi (PPI) ağının düğüm yerleştirmelerinin gen gerekliliğini tahmin etmeye yardımcı olup olamayacağını soruyoruz. İnsan geninin esaslığını tahmin etme konusundaki sonuçlarımız, düğüm gömülümlerinin tek başına %88'e kadar AUC skoruna ulaşabileceğini göstermektedir. Bu skor, gen özelliklerini karakterize etmek için topolojik özellikleri kullanılan modellerin başarımından ve önceki çalışma sonuçlarından daha iyidir. Ayrıca, türler arası homoloji bilgisi ile birleştiğinde, bu performansın %89 AUC skoruna ulaştığını gösteriyoruz. Çalışmamız, PPI ağındaki bir proteinin düğüm gömülümlerinin, proteinlerin ağ bağlantı modellerini yakaladığını ve gen esaslılık tahminlerini geliştirdiğini gösteriyor.

Keywords

Çizge gösterimleri, Düğüm gömülümleri, Gen esaslılığı, Ağ topolojik özellikleri, Protein-protein etkileşim ağı

Thanks

H. İ. Kuru İhsan Doğramacı Bilkent Üniversitesi Bilgisayar Mühendisliği Programının sağladığı bursa teşekkür eder. Y. i. Tepeli TUBITAK-BIDEB 2210-A bursuna teşekkür eder. Ö. T. BAGEP bursu için Bilim Akademisine teşekkür eder.

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Halil İbrahim Kuru This is me
0000-0003-4356-8846
Türkiye

Yasin İlkağan Tepeli This is me
0000-0002-3375-6678
Türkiye

Öznur Taştan ^*
0000-0001-7058-5372
Türkiye

Publication Date

July 31, 2022

Submission Date

November 26, 2021

Acceptance Date

March 1, 2022

Published in Issue

Year 2022 Volume: 10 Number: 3

DOI

https://doi.org/10.29130/dubited.1028387

IZ

https://izlik.org/JA55XW46JH

APA

Kuru, H. İ., Tepeli, Y. İ., & Taştan, Ö. (2022). GEGE: Predicting Gene Essentiality with Graph Embeddings. Duzce University Journal of Science and Technology, 10(3), 1567-1577. https://doi.org/10.29130/dubited.1028387

AMA

1.Kuru Hİ, Tepeli Yİ, Taştan Ö. GEGE: Predicting Gene Essentiality with Graph Embeddings. DUBİTED. 2022;10(3):1567-1577. doi:10.29130/dubited.1028387

Chicago

Kuru, Halil İbrahim, Yasin İlkağan Tepeli, and Öznur Taştan. 2022. “GEGE: Predicting Gene Essentiality With Graph Embeddings”. Duzce University Journal of Science and Technology 10 (3): 1567-77. https://doi.org/10.29130/dubited.1028387.

EndNote

Kuru Hİ, Tepeli Yİ, Taştan Ö (July 1, 2022) GEGE: Predicting Gene Essentiality with Graph Embeddings. Duzce University Journal of Science and Technology 10 3 1567–1577.

IEEE

[1]H. İ. Kuru, Y. İ. Tepeli, and Ö. Taştan, “GEGE: Predicting Gene Essentiality with Graph Embeddings”, DUBİTED, vol. 10, no. 3, pp. 1567–1577, July 2022, doi: 10.29130/dubited.1028387.

ISNAD

Kuru, Halil İbrahim - Tepeli, Yasin İlkağan - Taştan, Öznur. “GEGE: Predicting Gene Essentiality With Graph Embeddings”. Duzce University Journal of Science and Technology 10/3 (July 1, 2022): 1567-1577. https://doi.org/10.29130/dubited.1028387.

JAMA

1.Kuru Hİ, Tepeli Yİ, Taştan Ö. GEGE: Predicting Gene Essentiality with Graph Embeddings. DUBİTED. 2022;10:1567–1577.

MLA

Kuru, Halil İbrahim, et al. “GEGE: Predicting Gene Essentiality With Graph Embeddings”. Duzce University Journal of Science and Technology, vol. 10, no. 3, July 2022, pp. 1567-7, doi:10.29130/dubited.1028387.

Vancouver

1.Halil İbrahim Kuru, Yasin İlkağan Tepeli, Öznur Taştan. GEGE: Predicting Gene Essentiality with Graph Embeddings. DUBİTED. 2022 Jul. 1;10(3):1567-7. doi:10.29130/dubited.1028387

Cited By

Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models

Database

https://doi.org/10.1093/database/baaf027