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deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks
Open accessAbstract
With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.
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“This includes the use of AI in nearly every step of next-generation sequencing (NGS) processing and interpretation, including mutation identification (122, 123), artifact filtering (124), and driver gene identification (125).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [125]
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Cites
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936 · cited 1×
“An undirected network N is constructed by k -nearest neighbors (kNN) algorithm (Cover and Hart, 1967) in which every gene is connected to genes that have the k largest PCC scores with itself.”
MethodVogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863 · cited 2×
“These 12 features have been used in many machining learning-based methods (Vogelstein et al., 2013; Tokheim et al., 2016).”
MethodKumar, P., Henikoff, S., & Ng, P. C. (2009) · Nature Protocols · doi:10.1038/nprot.2009.86 · Cited by 6,521 · cited 1×
“CanDrA trains an SVM with 95 features obtained from 10 functional impact-based algorithms, such as SIFT (Kumar et al., 2009) and CHASM.”
MethodHuman Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,252 · cited 1×
“Considering that GINs are downloaded from online databases, such as BioGrid (Chatr-Aryamontri et al., 2017) and HPRD (Keshava Prasad et al., 2008), which contain many false positives, network-based methods need more accurate GIN to improve their prediction accuracy.”
MethodDiscovery and saturation analysis of cancer genes across 21 tumour types
Lawrence, M. S., Stojanov, P., Mermel, C. H., Robinson, J. T., Garraway, L. A., Golub, T. R., Meyerson, M., Gabriel, S. B., Lander, E. S., & Getz, G. (2014) · Nature · doi:10.1038/nature12912 · Cited by 3,096 · cited 3×
“Tokheim et al. propose a random forest algorithm (known as 20/20+) and compare it with seven classical driver gene prediction algorithms [ActiveDriver (Reimand and Bader, 2013), MuSiC (Dees et al., 2012), MutsigCV (Lawrence et al., 2014), OncodriveCLUST (Tamborero et al., 2013), OncodriveFM (Gonzalez-Perez and Lopez-B…”
MethodResultA systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088 · cited 1×
“Currently, two types of methods can be used to solve the imbalanced problem: data level methods and classifier level methods (Buda et al., 2018).”
MethodComprehensive Characterization of Cancer Driver Genes and Mutations
Bailey, M. H., Tokheim, C., Porta-Pardo, E., Sengupta, S., Bertrand, D., Weerasinghe, A., Colaprico, A., Wendl, M. C., Kim, J., Reardon, B., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.060 · Cited by 2,256
COSMIC: somatic cancer genetics at high-resolution
Forbes, S. A., Beare, D., Boutselakis, H., Bamford, S., Bindal, N., Tate, J., Cole, C. G., Ward, S., Dawson, E., Ponting, L., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1121 · Cited by 1,922 · cited 5×
“In addition to the driver genes collected from CGC, our deepDriver was also validated using the driver genes published in Bailey et al. (2018).”
MethodInfluentialToward a Shared Vision for Cancer Genomic Data
Grossman, R. L., Heath, A. P., Ferretti, V., Varmus, H. E., Lowy, D. R., Kibbe, W. A., & Staudt, L. M. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1607591 · Cited by 1,680 · cited 1×
“The mutation data and gene expression data of these three diseases were downloaded from the NCI Genomic Data Commons (GDC) (Grossman et al., 2016).”
MethodThe BioGRID interaction database: 2017 update
Chatr‐aryamontri, A., Oughtred, R., Boucher, L., Rust, J., Chang, C., Kolas, N. K., O’Donnell, L., Oster, S. K., Theesfeld, C. L., Sellam, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1102 · Cited by 985
Cited by
Sorted by citations
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429
“For example, deepDriver 46 trained similarity networks and a convolutional neural network (CNN) on mutation data simultaneously to predict driver genes with better performance than the competing approaches when applied in breast cancer and colorectal cancer.”
Breast cancer detection using artificial intelligence techniques: A systematic literature review
Nassif, A. B., Talib, M. W. A., Nasir, Q., Afadar, Y., & Elgendy, O. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102276 · Cited by 339
“[84] NCI Genomic Data Commons (GDC) Public Paid Https://gdc.”
BackgroundConvolutional neural network models for cancer type prediction based on gene expression
Mostavi, M., Chiu, Y., Huang, Y., & Chen, Y. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0677-2 · Cited by 282
Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction
Nasser, M., & Yusof, U. K. (2023) · Diagnostics · doi:10.3390/diagnostics13010161 · Cited by 249
“Additionally, for the purpose of studying and researching cancer, NCI Genomic Data Commons (GDC) [120] offers scholars a vast collection of gene-related data.”
BackgroundComputational network biology: Data, models, and applications
Liu, C., Ma, Y., Zhao, J., Nussinov, R., Zhang, Y., Cheng, F., & Zhang, Z. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.12.004 · Cited by 230
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196
Incorporating Machine Learning into Established Bioinformatics Frameworks
Auslander, N., Gussow, A. B., & Koonin, E. V. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22062903 · Cited by 151
“…genes, by incorporation of ML with statistical scoring of genomic sequencing [159], pathway-level mutations [160], mutation and gene interaction data [161], and by application of deep convolutional neural networks and random forest for analysis of mutations and gene similarity networks [162,163].”
MethodPeng, J., Li, J., & Shang, X. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03677-1 · Cited by 133
Cancer driver mutations: predictions and reality
Ostroverkhova, D. S., Przytycka, T. M., & Panchenko, A. R. (2023) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2023.03.007 · Cited by 147
Improving cancer driver gene identification using multi-task learning on graph convolutional network
Peng, W., Tang, Q., Dai, W., & Chen, T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab432 · Cited by 132
A novel network control model for identifying personalized driver genes in cancer
Guo, W.-F., Zhang, S., Zeng, T., Li, Y., Gao, J., & Chen, L. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007520 · Cited by 81
“On the other hand, the machine learning-based methods can be developed for any specific tasks depending on the available training data designed as pathogenic or neutral, but they have a few applications due to the probable incompleteness of their referred databases [6, 7].”
BackgroundKeeling, L. J., Winckler, C., Hintze, S., & Forkman, B. (2021) · Frontiers in Animal Science · doi:10.3389/fanim.2021.753080 · Cited by 88
Molecular Pathology of Lung CancerPreprint
Saller, J. J., & Boyle, T. A. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a037812 · Cited by 83
Agajanian, S., Odeyemi, O., & Verkhivker, G. M. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00044 · Cited by 57
“A deep learning-based method (deepDriver) predicts driver genes by CNN trained with mutation-based feature matrix constructed using similarity networks (Luo et al., 2019).”
MethodNetwork control principles for identifying personalized driver genes in cancer
Guo, W.-F., Zhang, S.-W., Zeng, T., Akutsu, T., & Chen, L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz089 · Cited by 44
Park, Y., Heider, D., & Hauschild, A.-C. (2021) · Cancers · doi:10.3390/cancers13133148 · Cited by 46
“It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11].”
MethodFawaz, A., Ferraresi, A., & Isidoro, C. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13111590 · Cited by 46
“Furthermore, the use of DNN models for large-scale data analysis enhanced the accuracy of computational models for the prediction of the muta-J tional landscape, molecular subtyping and drug repurposing [55–58].”
MethodMachine learning methods for prediction of cancer driver genes: a survey paper
de Andrades, R. S., & Recamonde‐Mendoza, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac062 · Cited by 41
“We also found four papers based on deep learning [36, 43, 48, 50] and three papers [25, 40, 51] adopting unsupervised learning techniques.”
BackgroundMethodInfluentialParrish, R. L., Gibson, G. C., Epstein, M. P., & Yang, J. (2021) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2021.100068 · Cited by 30
“2 region, a mutation found to be associated with shorter overall survival,(66) and was predicted to be a driver of lung adenocarcinoma (MIM: 211980).(65) The test region of FRG1EP overlaps with the test region of pseudogene ANKRD20A21P, another TWAS risk gene identified by TIGAR, which has been implicated as a potenti…”
BackgroundPeng, L., Bai, Z., Liu, L., Yang, L., Liu, X., Chen, M., & Chen, X. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3476120 · Cited by 32
Ahmad, R. M., Ali, B. R., Al-Jasmi, F. A., Sinnott, R., Dhaheri, N. A., & Mohamad, M. S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad479 · Cited by 30
“DeepDriver [106] is an ML–based tool based on deep neural networks that performs convolution of variant-based features of genes and their neighbors in similarity networks.”
Nourbakhsh, M., Degn, K., Saksager, A. B., Tiberti, M., & Papaleo, E. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad519 · Cited by 33
“Examples include FI-Net [63], DeepDriver [64] and Deep-Cues [65].”
BackgroundMethodMulti-Network Graph Contrastive Learning for Cancer Driver Gene Identification
Peng, W., Zhou, Z., Dai, W., Yu, N., & Wang, J. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3373652 · Cited by 29
“DeepDriver [20] extracts 12 features from gene DNA sequences that are closely related to cancer development and then splices the features of each gene with the features of its K nearest neighboring nodes in the co-expression network to form a feature matrix of each gene, and then input the gene…”
BackgroundRevolutionizing Genomics: Exploring the Potential of Next-Generation Sequencing
Abdi, G., Tarighat, M. A., Jain, M., Tendulkar, R., Tendulkar, M., & Barwant, M. M. (2024) · Springer eBooks · doi:10.1007/978-981-99-8401-5_1 · Cited by 30
DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-Omics Genomic Data
Zhang, S., Xu, J., & Zhang, T. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.004 · Cited by 31
“For example, deepDriver [26] predicted cancer driver genes with a convolutional neural network (CNN) model that was trained with the gene mutation features and their neighbors in the similarity networks.”
MethodNetwork Learning for Biomarker Discovery
Ding, Y., Fu, M., Luo, P., & Wu, F. (2023) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi0201004 · Cited by 28
Huang, X., Huang, K., Johnson, T. S., Radovich, M., Zhang, J., Ma, J., & Wang, Y. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab097 · Cited by 26
“To address this problem, there has been great interest recently to apply deep learning to model the complexity of cancer mutations on various cancer types (10–13).”
BackgroundFatima, N., & Rueda, L. G. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa500 · Cited by 24
Identifying Disease-Gene Associations With Graph-Regularized Manifold Learning
Luo, P., Xiao, Q., Wei, P.-J., Liao, B., & Wu, F. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00270 · Cited by 22
“The typical machine learning-based methods extract gene-related features and train models that can discriminate disease genes and passenger genes (Mordelet and Vert, 2011; Yang et al., 2012; Singh-Blom et al., 2013; Luo et al., 2019a,b).”
MethodMachine learning approaches for predicting biomolecule–disease associations
Ding, Y., Lei, X., Liao, B., & Wu, F. (2021) · Briefings in Functional Genomics · doi:10.1093/bfgp/elab002 · Cited by 22
Prediction of driver variants in the cancer genome via machine learning methodologies
Rogers, M. F., Gaunt, T. R., & Campbell, C. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa250 · Cited by 21
“DeepDriver [46], for example, based on use of a convolutional neural network.”
MethodPeng, W., Wu, R., Dai, W., & Yu, N. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05140-3 · Cited by 20
“DeepDriver [13] concatenates the features of the gene and its k nearest co-expression neighbours to generate a feature matrix for every gene.”
Background(2025) · doi:10.1038/s41467-025-58439-5 · Cited by 21
“DeepDriver [34] used gene mutation types as features, constructed a K-nearest neighbor graph based on the Pearson correlation coefficient, and trained a convolutional neural network to iden-84 tify cancer risk genes.”
Hosen, M. F., Mahmud, S., Ahmed, K., Chen, W., Moni, M. A., Deng, H., Shoombuatong, W., & Hasan, M. M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105433 · Cited by 16
Morabito, F., Adornetto, C., Monti, P., Amaro, A. A., Reggiani, F., Colombo, M., Aldana, Y. R., Tripepi, G. L., D’Arrigo, G., Vener, C., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1198992 · Cited by 17
Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network
Liu, C., Dai, Y., Yu, K., & Zhang, Z. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3063532 · Cited by 17
MiRNA–gene network embedding for predicting cancer driver genes
Peng, W., Wu, R., Dai, W., Ning, Y., Fu, X., Liu, L., & Liu, L. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac059 · Cited by 16
Jubran, J., Slutsky, R., Rozenblum, N., Rokach, L., Ben‐David, U., & Yeger‐Lotem, E. (2024) · Genome biology · doi:10.1186/s13059-024-03225-7 · Cited by 15
“In the context of cancer, supervised ML methods were applied to predict cancer driver genes [17, 18], to distinguish between cancer types [19, 20], and to predict gene dependency in tumors [21].”
MethodDeep Learning Techniques in Cancer Prediction Using Genomic Profiles
Bhonde, S. B., & Prasad, J. R. (2021) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct51068.2021.9417985 · Cited by 15
Chu, X., Guan, B., Dai, L.-Y., Liu, J., Li, F., & Shang, J. (2023) · BMC Genomics · doi:10.1186/s12864-023-09515-x · Cited by 14
“The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes [18].”
MethodWei, T., Fa, B., Luo, C., Johnston, L. J., Zhang, Y., & Yu, Z. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.613033 · Cited by 14
“DeepDriver predicts cancer driver genes based on mutation-based features and gene similarity networks using deep convolutional neural networks (Luo et al., 2019).”
BackgroundHassan, A., Alkhalifah, T., Alturise, F., & Khan, Y. D. (2022) · Diagnostics · doi:10.3390/diagnostics12123036 · Cited by 12
Meng, P., Wang, G., Guo, H., & Jiang, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106810 · Cited by 11
Parvandeh, S., Donehower, L. A., Katsonis, P., Hsu, T., Asmussen, J. K., Lee, K. D., & Lichtarge, O. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac215 · Cited by 11
Wadapurkar, R. M., Bapat, S., Mahajan, R. A., & Vyas, R. (2023) · Data Technologies and Applications · doi:10.1108/dta-03-2022-0096 · Cited by 11
XMR: an explainable multimodal neural network for drug response prediction
Wang, Z., Zhou, Y., Zhang, Y., Yu, M., & Wang, Y. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1164482 · Cited by 12
“DL has been successful in a variety of drug discovery tasks and may outperform traditional machine learning approaches in drug response prediction (Yuan et al., 2016; Luo et al., 2019; Sun et al., 2019; Jiao et al., 2020) despite being underexplored until recently.”
BackgroundYang, J., Fu, H., Xue, F., Li, M., Wu, Y., Yu, Z., Luo, H., Gong, J., Niu, X., & Zhang, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae418 · Cited by 10
“Methods like MuSiC [7],MutSigCV [8],deepDriver [9],and OncodriveCLUST [10] predict cancer genes through mutation analysis.”
MethodIdentifying cancer driver genes in individual tumours
Gillman, R., Field, M. A., Schmitz, U., Karamatic, R., & Hebbard, L. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.019 · Cited by 9
“Indeed, this approach has shown promise for large cohort data with several tools being recently developed (DriverML [68], DeepDriver [69], CHASM [30], and AI-Driver [70]).”
MethodCancerGATE: Prediction of cancer-driver genes using graph attention autoencoders
Jung, S., Wang, S., & Lee, D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108568 · Cited by 10
Trans-Driver: A Deep Learning Approach for Cancer Driver Gene Discovery With Multi-Omics Data
Yang, H., Yang, Z., Zhang, L., Yang, Y., Zhou, D., Li, D., Zhang, J., & Wang, Z. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3612010 · Cited by 9
Identification of cancer types from gene expressions using learning techniques
Bhonde, S. B., Wagh, S. K., & Prasad, J. R. (2022) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2022.2160243 · Cited by 9
“The last decade has witnessed abundant use of DL algorithms, which has the exciting potential to uncover complicated interactions buried in large-scale information including bioinformatics (Luo et al. 2019).”
BackgroundZhang, D., & Bin, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.607798 · Cited by 9
“DeepDriver employs deep learning to identify driver genes by estimating the functional impact of mutations (Luo et al., 2019 ).”
BackgroundGupta, P., Jindal, A., Ahuja, G., Jayadeva, & Sengupta, D. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102177 · Cited by 10
“Notably, AFF2 has recently been predicted to have driver roles (32).”
BackgroundZhang, S., Wang, Z., Li, Y., & Guo, W.-F. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04802-y · Cited by 9
Gangurde, R. A., Jagota, V., Khan, M. S., Sakthi, V. S., Boppana, U. M., Osei, B., & Kishore, K. H. (2023) · BioMed Research International · doi:10.1155/2023/6970256 · Cited by 10
“To effectively characterize tumours, machine learning and statistical techniques such as decision trees, k-nearest neighbour, support vector machines, and neural network techniques are being used [48].”
MethodRetractedMutBLESS: A tool to identify disease-prone sites in cancer using deep learning
Pandey, M., & Gromiha, M. M. (2023) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2023.166721 · Cited by 8
Ma, X., Li, Z., Du, Z., Xu, Y., Chen, Y., Zhuo, L., Fu, X., & Liu, R. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108484 · Cited by 7
Taheri, G., & Habibi, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108234 · Cited by 8
Zimmerman, L., Zelichov, O., Aizenman, A., Barbash, Z. S., Vidne, M., & Tarcic, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-61173-1 · Cited by 7
“In cancer genetics, deepDriver 26 is a notable example of the application of DCNNs for the task of cancer driver genes prediction.”
BackgroundWeighted minimum feedback vertex sets and implementation in human cancer genes detection
Li, R., Lin, C., Guo, W.-F., & Akutsu, T. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04062-2 · Cited by 7
IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning
Ali, Y., Sardaraz, M., Tahir, M., Elmannai, H., Hamdi, M., & Ksibi, A. (2023) · IEEE Access · doi:10.1109/access.2023.3259907 · Cited by 6
“Research paper [29] presents deepDriver framework that predicts cancer driver genes using a Convolutional Neural Network (CNN) based on somatic mutations.”
BackgroundZhang, B., & Fan, T. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.951939 · Cited by 8
“The framework by Mallik et al. integrated linear regression, differential expression, and deep learning and facilitated the robust interpretation of DNA methylation signatures and gene expression data for cervical cancer Mallik et al., (2020). Poirion et al., using multi-omics data, established a deep learning ensembl…”
MethodYang, H., Ding, Y., Tang, J., & Guo, F. (2021) · BMC Genomics · doi:10.1186/s12864-021-07864-z · Cited by 5
Zhang, L., Deng, T., Liufu, Z., Liu, X., Chen, B., Hu, Z., Liu, C., Tracy, M. E., Lu, X., Wen, H., et al. (2024) · eLife · doi:10.7554/elife.99340 · Cited by 5
Esmaeilzadeh, A. A., Kashian, M., Salman, H. M., Alsaffar, M. F., Jaber, M. M., Soltani, S., İlhan, A., & Bahrami, A. (2022) · Biology · doi:10.3390/biology11121851 · Cited by 3
RetractedChen, Y., Li, G., Liu, T., Zhou, R., Mao, Y., Wu, J., Liu, J., Hu, H., Wu, K., & Zhu, Q. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3574337 · Cited by 3
“Luo et al. proposed deepDriver, a convolutional neural network (CNN)- based method for cancer driver gene prediction, which integrates biological similarity networks with mutational functional impact features [10].”
Discovery of pan-cancer related genes via integrative network analysis
Zhu, Y., Zhang, H., Yang, Y., Zhang, C., Ou-Yang, L., Bai, L., Deng, M., Yi, M., Liu, S., & Wang, C. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac012 · Cited by 3
GATOmics: A Novel Multi-Omics Graph Attention Network Model for Cancer Driver Gene Detection
Kong, G., Wang, J., & Wang, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889716 · Cited by 3
“Methods like RLAG [18] and DeepDriver [19] have employed network representation learning to improve predictions, while Graph Convolutional Networks (GCN) [20], such as EMOGI [21] and MTGCN [22], have shown promise by integrating omics data with PPI networks.”
Predicting of Autism Spectrum Disorder using Gene Ontology
Ismail, E., Gad, W. K., & Hashem, M. (2021) · International Conference on Intelligent Computing and Information Systems (ICICIS) · doi:10.1109/icicis52592.2021.9694254 · Cited by 3
Xi, J., Deng, Z., Liu, Y., Wang, Q., & Shi, W. (2023) · PeerJ · doi:10.7717/peerj.14843 · Cited by 3
“Since the most relevant basis of cancer drivers is DNA aberrations in cancer samples, the existing computational driver detection approaches mainly focus on DNA sequencing data, and utilize the information of aberrations like single nucleotide variations [9, 10, 11], copy number alternations [12, 13], and structural v…”
InfluentialAgajanian, S. (2021) · Chapman University Digital Commons (Chapman University) · doi:10.36837/chapman.000220 · Cited by 3
A Novel Machine Learning Model for Identifying Patient-Specific Cancer Driver Genes
Jung, H., Choi, J., Park, J., & Ahn, J. (2022) · IEEE Access · doi:10.1109/access.2022.3176376 · Cited by 2
“The second group is based on machine learning models, which learn genetic or transcriptomic patterns of known driver mutations or genes [3]–[6].”
BackgroundHGTDG: An Interpretable Heterogeneous Graph Transformer Framework for Cancer Driver Gene Prediction
Xiong, S., Wang, Z., Zhang, J., Zhu, G., Zhang, Y., & Zou, Q. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385343 · Cited by 2
“Many efforts have been made to predict cancer driver genes through computational approaches, most based on machine learning algorithms [4]–[6].”
BackgroundLaganà, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55248-9_8 · Cited by 2
Capsule neural network and its applications in drug discovery
Wang, Y., Wang, B., Zou, J., Wu, A., Liu, Y., Wan, Y., Luo, J., & Wu, J. (2025) · iScience · doi:10.1016/j.isci.2025.112217 · Cited by 2
“78 Understanding the three-dimensional structure of targets is crucial for designing drugs to either inhibit or activate their functions.”
Mining the Human Metabolome for Precision Oncology Research
Edoho, M. E., Ekpenyong, M. E., Momodu, A. B., & Joseph, G. (2020) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3418094.3418123 · Cited by 2
“The present development has resulted in an increased driver genes validation using machine learning algorithms [7, 8, 11-19], and demonstrates the superiority of machine learning models, given limited knowledge of driver-disease associations.”
MethodYan, J., Hu, Z., Li, Z., Sun, S., & Guo, W.-F. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.891676 · Cited by 2
“(1) In gene mutation frequency-based methods, the mutation frequency of driver genes is usually assumed to be signi fi cantly higher than that of other genes (13, 47 – 51).”
BackgroundPark, J., & Jo, K. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3643887 · Cited by 1
“The comparative analysis results for individual cancers are shown in Table VI. DiffMut [6], GUST [8]), deepDriver [10], MODIG [13] are selected as the representatives of each category: statistical model, machine learning, deep learning with network information and graph neural network.”
Alotaibi, E. M., Alkhnbashi, O. S., & Tran, V. D. (2026) · Scientific Reports · doi:10.1038/s41598-026-36127-8 · Cited by 1
“To address these limitations, deep learning and machine learning techniques have been applied to learn complex mutational patterns from multi-omics data, including single-nucleotide variants (SNVs), copy number variations (CNVs), gene expression, and DNA methylation 11–13 .”
Yi, X., Han, Z., Lu, D., Xu, H., Zheng, X. S., Tang, Y., Li, J., Li, X., Liao, D., Li, H., et al. (2026) · Molecular Therapy · doi:10.1016/j.ymthe.2026.02.003 · Cited by 2
Yu, L., & Ren, S. (2022) · Scientia Sinica Vitae · doi:10.1360/ssv-2021-0324 · Cited by 1
GASN: gamma distribution test for driver genes identification based on similarity networks
Jiang, D., Wei, R., He, Z., Lin, S., Liu, C., & Lin, Y. (2023) · Connection Science · doi:10.1080/09540091.2023.2167937 · Cited by 1
“In the gene similarity network, genes and their adjacent genes also have similar properties (de Vries, 2020; Luo et al., 2019).”
BackgroundRuhela, V., Jena, L., Kaur, G., Gupta, R., & Gupta, A. (2023) · PubMed Central · Cited by 1
Advances in computational methods for identifying cancer driver genes
Wang, Y., Zhou, B., Ru, J., Meng, X., Wang, Y., & Liu, W. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023958 · Cited by 1
Xiong, S., Zhang, J., Luo, H., Zhang, Y., & Xiao, Q. (2024) · Methods · doi:10.1016/j.ymeth.2024.09.018 · Cited by 1
Saha, P., & Cerono, G. (2023) · Journal of Emerging Investigators · doi:10.59720/23-025 · Cited by 1
Zhang, L., Deng, T., Liufu, Z., Liu, X., Chen, B., Hu, Z., Liu, C., Tracy, M. E., Lu, X., Wen, H., et al. (2024) · eLife · doi:10.7554/elife.99340.2 · Cited by 1
Lischer, C., & Vera, J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.11605-8
Odeyemi, O. (2020) · Chapman University Digital Commons (Chapman University) · doi:10.36837/chapman.000155
“DeepDriver- A deep learning-based method predicts driver mutations by CNN trained with a mutation-based feature matrix constructed using similarity metrics (Luo et al., 2019).”
MethodIncorporating Machine Learning Into Bioinformatics Frameworks
Auslander, N., Gussow, A. B., & Koonin, E. V. (2021)
IMRDriver: coding and non-coding cancer driver genes identification based on network propagation
Wang, S., Huang, X., Chan, K. H. K., & Gao, L. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995249
“The mutation-based strategy, such as ActiveDriver[6], CoMEt[7], DriverML[8], MutSigCV[9], OncodriveCLUST[10], e-Driver[11], Dendrix[12], iPAC[13], MSEA[14], deepDriver[15], EPIMUTESTR[16] aims to identify CDGs by analyzing gene mutations and their related features.”
MethodBRDriver: Breast Cancer Driver Gene PredictorPreprint
Perera, R., & Perera, R. P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.09.523362
“Many computational tools and methods were developed using genetic data to identify the driver genes from the passenger genes [6].”
MethodAdoption of Artificial Intelligence in Human and Clinical Genomics
Jain, D. K., Zhang, L., Zhang, G., & Shukla, P. K. (2023) · Frontiers research topics · doi:10.3389/978-2-8325-2184-7
Tumor genomics in radiotherapy
Cui, S., Speers, C. W., & Naqa, I. E. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-822000-9.00007-0
Deep learning mediated identification of the origins of cancer of unknown primary
Divate, M. D. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.246033
Zhang, S., Wang, Z., Li, Y., & Guo, W.-F. (2022) · Figshare · doi:10.6084/m9.figshare.20499900
Zhang, S., Wang, Z., Li, Y., & Guo, W.-F. (2022) · Figshare · doi:10.6084/m9.figshare.20499894
Metadata
- Authors
- Luo, P., Ding, Y., Lei, X., & Wu, F.-X.
- Affiliations
- University of Saskatchewan (CA)
- Shaanxi Normal University (CN)
- Hainan Normal University (CN)
- Corresponding author
- Wu, Fang-Xiang
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2019-01-29
- Volume
- 10
- Pages
- 13-13
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC6361806
- OpenAlex
- W2913030342
- Cited by
- 107
- References
- 54
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 19 · 2023: 30 · 2022: 17 · 2021: 17 · 2020: 9 · 2019: 5
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- China Scholarship Council
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- cancer driver genes, somatic mutations, deep convolutional neural networks, similarity networks, multi-omics integration, breast cancer, colorectal cancer, oncogenic mutations
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