دورية أكاديمية
CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders.
| العنوان: | CancerGATE: Prediction of cancer-driver genes using graph attention autoencoders. |
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| المؤلفون: | Jung S; Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea. Electronic address: sktoyo@kaist.ac.kr., Wang S; Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea. Electronic address: kingsarrow@kaist.ac.kr., Lee D; Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea. Electronic address: dhlee@kaist.ac.kr. |
| المصدر: | Computers in biology and medicine [Comput Biol Med] 2024 Jun; Vol. 176, pp. 108568. Date of Electronic Publication: 2024 May 09. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York : Elsevier Original Publication: New York, Pergamon Press. |
| مواضيع طبية MeSH: | Neoplasms*/genetics , Deep Learning*, Humans ; Computational Biology/methods ; Software |
| مستخلص: | Discovery of the cancer type specific-driver genes is important for understanding the molecular mechanisms of each cancer type and for providing proper treatment. Recently, graph deep learning methods became widely used in finding cancer-driver genes. However, previous methods had limited performance in individual cancer types due to a small number of cancer-driver genes used in training and biases toward the cancer-driver genes used in training the models. Here, we introduce a novel pipeline, CancerGATE that predicts the cancer-driver genes using graph attention autoencoder (GATE) to learn in a self-supervised manner and can be applied to each of the cancer types. CancerGATE utilizes biological network topology and multi-omics data from 15 types of cancer of 20,079 samples from the cancer genome atlas (TCGA). Attention coefficients calculated in the model are used to prioritize cancer-driver genes by comparing coefficients of cancer and normal contexts. CancerGATE shows a higher AUPRC with a difference ranging from 1.5 % to 36.5 % compared to the previous graph deep learning models in each cancer type. We also show that CancerGATE is free from the bias toward cancer-driver genes used in training, revealing mechanisms of the cancer-driver genes in specific cancer types. Finally, we propose novel cancer-driver gene candidates that could be therapeutic targets for specific cancer types. Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest. (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Attention mechanism; Cancer-driver gene; Graph convolutional network; Interpretability; Multi-omics data; Self-supervised learning |
| تواريخ الأحداث: | Date Created: 20240514 Date Completed: 20240529 Latest Revision: 20240529 |
| رمز التحديث: | 20240530 |
| DOI: | 10.1016/j.compbiomed.2024.108568 |
| PMID: | 38744009 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1879-0534 |
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| DOI: | 10.1016/j.compbiomed.2024.108568 |