An interpretable molecular framework for predicting cancer driver missense mutations
Open accessPublished version of a preprint
Abstract
Missense mutations play a critical role in human disease, contributing to both inherited disorders and cancer. However, accurately predicting their functional impact—particularly for cancer driver mutations—remains a major challenge due to limited validated labels and the complex molecular basis of oncogenesis. Here, we systematically characterized over 120,000 missense variants across pathogenic, benign, driver, passenger, recurrent somatic, and common population classes, using a comprehensive set of mechanistically grounded molecular features. By assessing the statistical burden of variations, we demonstrated that these features effectively discriminate among diverse variant classes and reveal a consistent enrichment of functional sites, structural integrity, and biophysical changes in pathogenic and driver mutations. Building on these insights, we developed MutaPheno, an interpretable framework for predicting the functional consequences of missense mutations. The model integrates 34 molecular-level features, encompassing structural, functional, physicochemical, and contextual descriptors, using a random forest algorithm. Trained exclusively on pathogenic and benign variants, MutaPheno achieved strong accuracy in predicting cancer driver mutations, outperforming both cancer-specific and general pathogenicity tools, while also demonstrating superior robustness when tested on unseen proteins. Our findings highlight the shared mechanisms between pathogenic and driver mutations and emphasize the role of molecular features in improving variant interpretation. MutaPheno provides a transparent and generalizable tool that can facilitate driver discovery and the development of targeted therapies.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega
Sievers, F., Wilm, A., Dineen, D., Gibson, T., Karplus, K., Li, W.-Z., Lopez, R., McWilliam, H., Remmert, M., Söding, J., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.75 · Cited by 15,576 · see citing works
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864 · see citing works
A method and server for predicting damaging missense mutations.
Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194 · see citing works
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587 · see citing works
Vogelstein, 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 · see citing works
Exploring Network Structure, Dynamics, and Function Using NetworkX
Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008) · Proceedings of the Python in Science Conference · doi:10.25080/tcwv9851 · Cited by 7,477 · see citing works
UniProt: the Universal Protein Knowledgebase in 2023
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210 · see citing works
SIFT: predicting amino acid changes that affect protein function
Ng, P. C. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg509 · Cited by 6,601 · see citing works
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170 · see citing works
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M., & Söding, J. (2017) · Nature Biotechnology · doi:10.1038/nbt.3988 · Cited by 4,840 · see citing works
ClinVar: public archive of relationships among sequence variation and human phenotype
Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., & Maglott, D. R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1113 · Cited by 3,516 · see citing works
Stratton, M. R., Campbell, P. J., & Futreal, P. A. (2009) · Nature · doi:10.1038/nature07943 · Cited by 3,459 · see citing works
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations
Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M., Latham, V. M., & Skrzypek, E. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1267 · Cited by 3,243 · see citing works
Interaction revisited: the difference between two estimates.
Altman, D. G., & Bland, J. M. (2003) · BMJ · doi:10.1136/bmj.326.7382.219 · Cited by 3,203 · see citing works
Pan-cancer analysis of whole genomes
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Aaltonen, L. A., Abascal, F., Abeshouse, A., Aburatani, H., Adams, D. J., Agrawal, N., Ahn, K. S., Ahn, S.-M., Aikata, H., et al. (2020) · Nature · doi:10.1038/s41586-020-1969-6 · Cited by 3,064 · see citing works
REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
Ioannidis, N. M., Rothstein, J. H., Pejaver, V., Middha, S., McDonnell, S. K., Baheti, S., Musolf, A., Li, Q., Holzinger, E., Karyadi, D., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.08.016 · Cited by 2,908 · see citing works
A subcellular map of the human proteome
Thul, P. J., Åkesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., Alm, T., Asplund, A., Björk, L., Breckels, L. M., et al. (2017) · Science · doi:10.1126/science.aal3321 · Cited by 2,813 · see citing works
OncoKB: A Precision Oncology Knowledge Base
Chakravarty, D., Gao, J., Phillips, S. M., Kundra, R., Zhang, H., Wang, J., Rudolph, J. E., Yaeger, R. D., Soumerai, T. E., Nissan, M. H., et al. (2017) · JCO Precision Oncology · doi:10.1200/po.17.00011 · Cited by 2,735 · see citing works
Comprehensive 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 · see citing works
Self-citationAccurate proteome-wide missense variant effect prediction with AlphaMissense
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163 · see citing works
Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes
Initiative, A. P. C. G., Biankin, A. V., Waddell, N., Kassahn, K. S., Gingras, M.-C., Muthuswamy, L. B., Johns, A. L., Miller, D. K., Wilson, P. J., Patch, A.-M., et al. (2012) · Nature · doi:10.1038/nature11547 · Cited by 2,058 · see citing works
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · cited 1× · see citing works
“…sites and predicted PPI - BS were obtained from exter-nal databases, while the others were computed in - house using experimentally resolved PDB structures (68) and/or AlphaFold2 - predicted monomeric models (69, 70); models that were truncated or had a median pLDDT score below 70 were excluded.”
<scp>PANTHER</scp> : Making genome‐scale phylogenetics accessible to all
Thomas, P. D., Ebert, D., Muruganujan, A., Mushayahama, T., Albou, L., & Mi, H. (2022) · Protein Science · doi:10.1002/pro.4218 · Cited by 2,010 · see citing works
The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers
Sondka, Z., Bamford, S., Cole, C. G., Ward, S. A., Dunham, I., & Forbes, S. A. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0060-1 · Cited by 1,597 · see citing works
ClinGen — The Clinical Genome Resource
Rehm, H. L., Berg, J. S., Brooks, L. D., Bustamante, C. D., Evans, J. P., Landrum, M. J., Ledbetter, D. H., Maglott, D. R., Martin, C. L., Nussbaum, R. L., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmsr1406261 · Cited by 1,496 · see citing works
Steyerberg, E. W. (2009) · Statistics for Biology and Health · doi:10.1007/978-0-387-77244-8 · Cited by 1,491 · see citing works
A compendium of mutational cancer driver genes
Martínez-Jiménez, F., Muiños, F., Sentís, I., Deu-Pons, J., Reyes-Salazar, I., Arnedo-Pac, C., Mularoni, L., Pich, O., Bonet, J., Kranas, H., et al. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-0290-x · Cited by 1,278 · see citing works
InterPro: the protein sequence classification resource in 2025
Blum, M., Andreeva, A. E., Florentino, L. C., Chuguransky, S., Grego, T., Hobbs, E., Pinto, B. L., Orr, A., Paysan‐Lafosse, T., Ponamareva, I., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1082 · Cited by 1,180 · see citing works
Pathogenesis and Treatment of Sickle Cell Disease
Bunn, H. F. (1997) · New England Journal of Medicine · doi:10.1056/nejm199709113371107 · Cited by 1,154 · see citing works
Pan-cancer whole-genome analyses of metastatic solid tumours
Priestley, P., Baber, J., Lolkema, M. P., Steeghs, N., de Bruijn, E., Shale, C., Duyvesteyn, K., Haidari, S., van Hoeck, A., Onstenk, W., et al. (2019) · Nature · doi:10.1038/s41586-019-1689-y · Cited by 1,106 · cited 2× · see citing works
“In contrast, only highly recurrent cross - cancer mutations—those arising independently across diverse cancer types—show molecular profiles closely resembling established drivers, supporting the notion that pan - cancer recurrence reflects stronger positive selection (39 , 86 , 87).”
Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines
Ellrott, K., Bailey, M. H., Saksena, G., Covington, K. R., Kandoth, C., Stewart, C., Hess, J. M., Ma, S., Chiotti, K., McLellan, M. D., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.03.002 · Cited by 826 · see citing works
Self-citationdbNSFP: A lightweight database of human nonsynonymous SNPs and their functional predictions
Liu, X., Jian, X., & Boerwinkle, E. (2011) · Human Mutation · doi:10.1002/humu.21517 · Cited by 816 · see citing works
Chang, M. T., Asthana, S., Gao, S. P., Lee, B. H., Chapman, J. S., Kandoth, C., Gao, J., Socci, N. D., Solit, D. B., Olshen, A. B., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3391 · Cited by 808 · see citing works
The role of BRAF V600 mutation in melanoma
Ascierto, P. A., Kirkwood, J. M., Grob, J., Simeone, E., Grimaldi, A. M., Maio, M., Palmieri, G., Testori, A., Marincola, F. M., & Mozzillo, N. (2012) · Journal of Translational Medicine · doi:10.1186/1479-5876-10-85 · Cited by 804 · see citing works
Stenson, P. D., Mort, M. E., Ball, E. V., Chapman, M., Evans, K., Azevedo, L., Hayden, M., Heywood, S., Millar, D. S., Phillips, A. D., et al. (2020) · Human Genetics · doi:10.1007/s00439-020-02199-3 · Cited by 793 · see citing works
Repana, D., Nulsen, J., Dreßler, L., Bortolomeazzi, M., Venkata, S. K., Tourna, A., Yakovleva, A., Palmieri, T., & Ciccarelli, F. D. (2019) · Genome biology · doi:10.1186/s13059-018-1612-0 · Cited by 677 · see citing works
Liu, X., Li, C., Mou, C., Dong, Y., & Tu, Y.-C. (2020) · Genome Medicine · doi:10.1186/s13073-020-00803-9 · Cited by 652 · see citing works
Krivák, R., & Hoksza, D. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0285-8 · Cited by 597 · see citing works
Identifying Mendelian disease genes with the Variant Effect Scoring Tool
Carter, H. K., Douville, C. B., Stenson, P. D., Cooper, D. N., & Karchin, R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s3-s3 · Cited by 566 · see citing works
Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations
Tamborero, D., Rubio-Pérez, C., Deu-Pons, J., Schroeder, M. P., Vivancos, A. P., Rovira, A., Tusquets, I., Albanell, J., Rodon, J. A., Tabernero, J. M., et al. (2018) · Genome Medicine · doi:10.1186/s13073-018-0531-8 · Cited by 523 · see citing works
Mutant p53 in cancer: from molecular mechanism to therapeutic modulation
Chen, X., Zhang, T., Su, W., Dou, Z., Zhao, D., Jin, X., Lei, H., Wang, J., Xie, X., Cheng, B., et al. (2022) · Cell Death & Disease · doi:10.1038/s41419-022-05408-1 · Cited by 494 · see citing works
Carter, H. K., Chen, S., Işık, L., Tyekucheva, S., Velculescu, V. E., Kinzler, K. W., Vogelstein, B., & Karchin, R. (2009) · Cancer Research · doi:10.1158/0008-5472.can-09-1133 · Cited by 448 · see citing works
Molecular Mechanisms of Disease-Causing Missense Mutations
Stefl, S. K., Nishi, H., Petukh, M. G., Panchenko, A. R., & Alexov, E. G. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.07.014 · Cited by 334 · see citing works
Gerasimavicius, L., Livesey, B., & Marsh, J. A. (2022) · Nature Communications · doi:10.1038/s41467-022-31686-6 · Cited by 309 · see citing works
PremPS: Predicting the impact of missense mutations on protein stability
Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., & Li, M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008543 · Cited by 257 · see citing works
Self-citationComprehensive characterization of protein–protein interactions perturbed by disease mutations
Cheng, F., Zhao, J., Wang, Y., Lü, W., Liu, Z., Zhou, Y., Martin, W. R., Wang, R., Huang, J. Y., Hao, T., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00774-y · Cited by 216 · see citing works
MutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions
Zhang, N., Chen, Y., Lu, H., Zhao, F., Alvarez, R. V., Goncearenco, A., Panchenko, A. R., & Li, M. (2020) · iScience · doi:10.1016/j.isci.2020.100939 · Cited by 199 · cited 1× · see citing works
“…enrichment patterns of carefully selected molecular features can improve driver prioritization and help identify potential driver mutations, we systematically stratified recurrent somatic mutations by cancer - type recurrence patterns and gene - level categories and performed enrichment analyses.”
Self-citationELM—the Eukaryotic Linear Motif resource—2024 update
Kumar, M., Michael, S., Alvarado-Valverde, J., Zeke, A., Lázár, T., Glavina, J., Nagy‐Kanta, E., Donagh, J. M., Kálmán, Z. E., Pascarelli, S., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1058 · Cited by 185 · see citing works
Xia, Y., Xia, C., Pan, X., & Shen, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab044 · Cited by 179 · see citing works
Improved Method for the Identification and Validation of Allosteric Sites
Song, K., Liu, X., Huang, W., Lu, S., Shen, Q., Zhang, L., & Zhang, J. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00014 · Cited by 152 · see citing works
Self-citationInteractome INSIDER: a structural interactome browser for genomic studies
Meyer, M. J., Beltrán, J. F., Liang, S., Fragoza, R., Rumack, A., Liang, J. H., Wei, X., & Yu, H. (2018) · Nature Methods · doi:10.1038/nmeth.4540 · Cited by 152 · see citing works
DisProt in 2024: improving function annotation of intrinsically disordered proteins
Aspromonte, M. C., Nugnes, M. V., Quaglia, F., Bouharoua, A., Sagris, V., Promponas, V. J., Chasapi, A., Fichó, E., Balatti, G. E., Parisi, G. D., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad928 · Cited by 142 · see citing works
UniProt Tools: BLAST, Align, Peptide Search, and ID Mapping
Zaru, R., & Orchard, S. E. (2023) · Current Protocols · doi:10.1002/cpz1.697 · Cited by 138 · see citing works
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 138 · see citing works
CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers
Tokheim, C. J., & Karchin, R. (2019) · Cell Systems · doi:10.1016/j.cels.2019.05.005 · Cited by 126 · see citing works
Liao, J., Yang, B., Zhang, Y., Wang, X., Ye, Y., Peng, J.-W., Yang, Z.-Z., He, J., Zhang, Y., Hu, K., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz823 · Cited by 122 · see citing works
Iqbal, S., Pérez‐Palma, E., Jespersen, J. B., May, P., Hoksza, D., Heyne, H., Ahmed, S. S., Rifat, Z. T., Rahman, M. S., Lage, K., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002660117 · Cited by 120 · see citing works
The Function of the Mutant p53-R175H in Cancer
Chiang, Y.-T., Chien, Y., Lin, Y., Wu, H.-H., Lee, D., & Yu, Y. (2021) · Cancers · doi:10.3390/cancers13164088 · Cited by 119 · see citing works
MutaBind estimates and interprets the effects of sequence variants on protein–protein interactions
Li, M., Simonetti, F. L., Goncearenco, A., & Panchenko, A. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw374 · Cited by 118 · see citing works
Self-citationPires, D. E. V., Chen, J., Blundell, T. L., & Ascher, D. B. (2016) · Scientific Reports · doi:10.1038/srep19848 · Cited by 102 · see citing works
The Proteins API: accessing key integrated protein and genome information
Nightingale, A., Antunes, R., Alpi, E., Bursteinas, B., Gonzales, L. J. D. C., Liu, W., Luo, J., Qi, G., Turner, E., & Martin, M. J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx237 · Cited by 97 · see citing works
OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers
Wang, T., Ruan, S., Zhao, X., Shi, X., Teng, H., Zhong, J., You, M., Xia, K., Sun, Z. S., & Mao, F. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1033 · Cited by 93 · see citing works
Finding driver mutations in cancer: Elucidating the role of background mutational processes
Brown, A., Li, M., Goncearenco, A., & Panchenko, A. R. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006981 · Cited by 90 · see citing works
Self-citationPassenger Hotspot Mutations in Cancer
Hess, J. M., Bernards, A., Kim, J., Miller, M. L., Taylor-Weiner, A. N., Haradhvala, N. J., Lawrence, M. S., & Getz, G. A. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.08.002 · Cited by 87 · see citing works
Piovesan, D., Conte, A. D., Mehdiabadi, M., Aspromonte, M. C., Blum, M., Tesei, G., von Bülow, S., Lindorff‐Larsen, K., & Tosatto, S. C. E. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae969 · Cited by 81 · see citing works
Insights into the mechanism of the PIK3CA E545K activating mutation using MD simulations
Leontiadou, H., Galdadas, I., Athanasiou, C., & Cournia, Z. (2018) · Scientific Reports · doi:10.1038/s41598-018-27044-6 · Cited by 68 · see citing works
Reimand, J., Wagih, O., & Bader, G. D. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1004919 · Cited by 65 · see citing works
Chung, C., Tang, Y., Chiu, Y.-P., Li, S., Hsieh, W.-K., Yao, L., Chiang, Y., Pang, Y., Chen, G.-T., Chou, K.-C., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1005 · Cited by 57 · see citing works
Comprehensive assessment of computational algorithms in predicting cancer driver mutations
Chen, H., Li, J., Wang, Y., Ng, P. K., Tsang, Y. H., Shaw, K. R. M., Mills, G. B., & Liang, H. (2020) · Genome biology · doi:10.1186/s13059-020-01954-z · Cited by 55 · see citing works
Bayrak, Ç. S., Stein, D. F., Jain, A., Chaudhary, K., Nadkarni, G. N., Vleck, T. T. V., Puel, A., Boisson‐Dupuis, S., Okada, S., Stenson, P. D., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.10.007 · Cited by 55 · see citing works
Unsupervised detection of cancer driver mutations with parsimony-guided learning
Kumar, R. D., Swamidass, S. J., & Bose, R. (2016) · Nature Genetics · doi:10.1038/ng.3658 · Cited by 49 · see citing works
Sun, T., Chen, Y., Wen, Y., Zhu, Z., & Li, M. (2021) · Communications Biology · doi:10.1038/s42003-021-02826-3 · Cited by 43 · see citing works
Self-citationNetSlim: high-confidence curated signaling maps
Raju, R., Nanjappa, V., Balakrishnan, L., Radhakrishnan, A., Thomas, J. K., Sharma, J. N., Mi, T., Palapetta, S. M., Subbannayya, T., Sekhar, N. R., et al. (2011) · Database · doi:10.1093/database/bar032 · Cited by 41 · see citing works
Advances in anti-BRAF therapies for lung cancer
Roviello, G., D’Angelo, A., Sirico, M., Pittacolo, M., Conter, F. U., & Sobhani, N. (2021) · Investigational New Drugs · doi:10.1007/s10637-021-01068-8 · Cited by 40 · see citing works
Li, M., Kales, S. C., Ma, K., Shoemaker, B. A., Crespo-Barreto, J., Cangelosi, A. L., Lipkowitz, S., & Panchenko, A. R. (2015) · Cancer Research · doi:10.1158/0008-5472.can-14-3812 · Cited by 37 · see citing works
Self-citationAI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
Wang, H., Wang, T., Zhao, X., Wu, H., You, M., Sun, Z. S., & Mao, F. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa084 · Cited by 35 · see citing works
Computational Approaches to Prioritize Cancer Driver Missense Mutations
Zhao, F., Zheng, L., Goncearenco, A., Panchenko, A. R., & Li, M. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19072113 · Cited by 33 · see citing works
Self-citationKiel, C., Benisty, H., Lloréns‐Rico, V., & Serrano, L. G. (2016) · eLife · doi:10.7554/elife.12814 · Cited by 32 · see citing works
Laddach, A., Ng, J. C.-F., & Fraternali, F. (2021) · PLoS Biology · doi:10.1371/journal.pbio.3001207 · Cited by 25 · see citing works
ENPD - A Database of Eukaryotic Nucleic Acid Binding Proteins: Linking Gene Regulations to Proteins
Leung, R. W. T., Jiang, X., Chu, K. H., & Qin, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1112 · Cited by 20 · see citing works
Missense Variants Reveal Functional Insights Into the Human ARID Family of Gene Regulators
Deák, G., & Cook, A. G. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167529 · Cited by 20 · see citing works
Zheng, F., Jiang, X., Wen, Y., Yang, Y., & Li, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.018 · Cited by 14 · see citing works
Self-citationAre Next-Generation Pathogenicity Predictors Applicable to Cancer?
Ostroverkhova, D., Sheng, Y., & Panchenko, A. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168644 · Cited by 8 · see citing works
Wierbowski, S. D., Fragoza, R., Liang, S., & Yu, H. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.09.006 · Cited by 7 · see citing works
Flagged first, then by citations
1 of the 92 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Luo, D., Zheng, J., Du, X.-K., Xia, D., Zhang, M., & Liang, Z. T. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01767
Liu, Y., Zhang, J., & Li, M. (2026) · Protein Science · doi:10.1002/pro.70728
Self-citation
- Authors
- Yang, Y., Sun, W., Liu, Y., Zhang, J., & Li, M.
- Affiliations
- Soochow University (CN)
- Corresponding author
- Li, Minghui
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2026-02-03
- Volume
- 123
- Issue
- 6
- Pages
- e2524289123-e2524289123
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC12890849
- OpenAlex
- W7127438624
- Cited by
- 3
- References
- 92
- Citations by year
- 2026: 3
- FWCI
- 12.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Keywords
- missense mutations, cancer driver mutations, variant pathogenicity prediction, molecular features, random forest, model interpretability, somatic mutations, recurrent somatic mutations, protein structure, physicochemical properties, variant interpretation
- MeSH terms
- Algorithms, Humans, Neoplasms, Computational Biology, Mutation, Missense
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
