Big data in basic and translational cancer research

Hallmarks of Cancer: The Next Generation

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PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer

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The Elements of Statistical Learning

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The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data

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Integrated analysis of multimodal single-cell data

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Comprehensive Integration of Single-Cell Data

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Tissue-based map of the human proteome

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Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

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Robust enumeration of cell subsets from tissue expression profiles

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

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Artificial intelligence for digital and computational pathology

Song, A. H., Jaume, G., Williamson, D. F. K., Lu, M. Y., Vaidya, A., Miller, T. R., & Mahmood, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00096-8 · Cited by 356

A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches

Baião, A. R., Cai, Z., Poulos, R. C., Robinson, P. J., Reddel, R. R., Zhong, Q., Vinga, S., & Gonçalves, E. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf355 · Cited by 274

Biomarkers for immunotherapy of hepatocellular carcinoma

Greten, T. F., Villanueva, A., Korangy, F., Ruf, B., Yarchoan, M., Ma, L., Ruppin, E., & Wang, X. W. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00816-4 · Cited by 227

Assessing and mitigating batch effects in large-scale omics studies

Yu, Y., Mai, Y., Zheng, Y., & Shi, L. M. (2024) · Genome biology · doi:10.1186/s13059-024-03401-9 · Cited by 254

Advances in molecular pathology and therapy of non-small cell lung cancer

Huang, Q., Li, Y., Huang, Y., Wu, J., Bao, W., Xue, C., Li, X., Dong, S., Dong, Z., & Hu, S. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02243-6 · Cited by 197

Multimodal data integration for oncology in the era of deep neural networks: a review

Waqas, A., Tripathi, A., Ramachandran, R. P., Stewart, P. A., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1408843 · Cited by 165

Advancing CAR T cell therapy through the use of multidimensional omics data

Yang, J., Chen, Y., Jing, Y., Green, M. R., & Han, L. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00729-2 · Cited by 130

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges

Mao, Y., Shangguan, D., Huang, Q., Xiao, L., Cao, D., Zhou, H., & Wang, Y. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02321-x · Cited by 90

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Matsuoka, T., & Yashiro, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25115880 · Cited by 83

Omics-Based Investigations of Breast Cancer

Neagu, A.-N., Whitham, D., Bruno, P. S., Morrissiey, H., Darie, C. A., & Darie, C. C. (2023) · Molecules · doi:10.3390/molecules28124768 · Cited by 81

Big data and artificial intelligence in cancer research

Wu, X., Li, W., & Tu, H. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.10.006 · Cited by 83

Computational approaches to modelling and optimizing cancer treatment

McDonald, T. O., Cheng, Y.-C., Graser, C. J., Nicol, P. B., Temko, D., & Michor, F. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00089-7 · Cited by 83

Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review

Poursaeed, R., Mohammadzadeh, M., & Safaei, A. A. (2024) · BMC Cancer · doi:10.1186/s12885-024-13320-4 · Cited by 76

Hallmarks of artificial intelligence contributions to precision oncology

Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 83

Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review

Dhanka, S., Sharma, A., Kumar, A., Maini, S., & Vundavilli, H. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10309-5 · Cited by 87

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Ahmed, F., Samantasinghar, A., Soomro, A. M., Kim, S., & Choi, K. H. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104373 · Cited by 78

A multi-omics approach to reveal critical mechanisms of activator protein 1 (AP-1)

Li, F., Tian, J., Zhang, L., He, H., & Song, D. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.117225 · Cited by 72

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

Biswas, A., Kumari, A., Gaikwad, D. S., & Pandey, D. K. (2023) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2023.0197 · Cited by 56

Cancer immunology data engine reveals secreted AOAH as a potential immunotherapy

Gong, L., Luo, J., Yang, E., Ru, B., Qi, Z., Yang, Y., Rani, A., Purohit, A., Zhang, Y., Guan, G., et al. (2025) · Cell · doi:10.1016/j.cell.2025.07.004 · Cited by 56

Progress and Perspective of CRISPR‐Cas9 Technology in Translational Medicine

Zheng, R., Zhang, L., Parvin, R., Su, L., Chi, J., Shi, K., Ye, F., & Huang, X. (2023) · Advanced Science · doi:10.1002/advs.202300195 · Cited by 56

scDiffusion: conditional generation of high-quality single-cell data using diffusion model

Luo, E., Hao, M., Wei, L., & Zhang, X. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae518 · Cited by 66

Artificial Intelligence in Public Health: Current Trends and Future Possibilities

Giansanti, D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191911907 · Cited by 46

Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine

Jose, A., Kulkarni, P., Thilakan, J., Munisamy, M., Malhotra, A. G., Singh, J., Kumar, A., Rangnekar, V. M., Arya, N., & Rao, M. (2024) · Molecular Cancer · doi:10.1186/s12943-023-01916-6 · Cited by 49

Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects

Cheng, Y., Qu, Z., Jiang, Q., Xu, T., Zheng, H., Ye, P., He, M., Tong, Y., Ma, Y., & Bao, A. (2023) · Advanced Materials · doi:10.1002/adma.202305095 · Cited by 43

Pathogenesis and precision medicine for predicting response in inflammatory bowel disease: advances and future directions

Little, R. D., Jayawardana, T. C., Koentgen, S., Zhang, F., Connor, S. J., Boussioutas, A., Ward, M. G., Gibson, P. R., Sparrow, M., & Hold, G. L. (2024) · eGastroenterology · doi:10.1136/egastro-2023-100006 · Cited by 42

Recommendations for reporting tissue and circulating tumour (ct)DNA next-generation sequencing results in non-small cell lung cancer

Malapelle, U., Leighl, N. B., Addeo, A., Hershkovitz, D., Hochmair, M. J., Khorshid, O., Länger, F. P., de Marinis, F., Peled, N., Sheffield, B. S., et al. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02709-4 · Cited by 39

Biomimetic Targeted Co‐Delivery System Engineered from Genomic Insights for Precision Treatment of Osteosarcoma

Luo, T., Fan, Z., Zeng, A., Wang, A., Pan, Y., Xu, Y., Chen, H., Chen, W., Nie, D., Lin, J., et al. (2024) · Advanced Science · doi:10.1002/advs.202410427 · Cited by 38

Advancing breast, lung and prostate cancer research with federated learning. A systematic review

Ankolekar, A., Boie, S. D., Abdollahyan, M., Gadaleta, E., Hasheminasab, S. A., Yang, G., Beauville, C., Δικαίος, Ν., Kastis, G. A., Bussmann, M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01591-5 · Cited by 44

Immune Resistance Mechanisms and the Road to Personalized Immunotherapy

Piper, M., Kluger, H. M., Ruppin, E., & Hu‐Lieskovan, S. (2023) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_390290 · Cited by 34

Lipocalin-2: A Nurturer of Tumor Progression and a Novel Candidate for Targeted Cancer Therapy

Živalj, M., Ginderachter, J. A. V., & Stijlemans, B. (2023) · Cancers · doi:10.3390/cancers15215159 · Cited by 35

DNA methylation in breast cancer: early detection and biomarker discovery through current and emerging approaches

Hum, M., & Lee, A. S. G. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06495-2 · Cited by 35

Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology

Gorman, C., Punzo, D., Octaviano, I., Pieper, S. D., Longabaugh, W. J. R., Clunie, D., Kikinis, R., Fedorov, A., & Herrmann, M. D. (2023) · Nature Communications · doi:10.1038/s41467-023-37224-2 · Cited by 36

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Casotti, M. C., Meira, D., Zetum, A., de Araújo, B. C., da Silva, D. R. C., dos Santos, E. D. V. W., Garcia, F. M., de Paula, F., Santana, G. M., Louro, L. S., et al. (2023) · Genes · doi:10.3390/genes14040801 · Cited by 28

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics

Zheng, L., Shi, S., Sun, X., Lu, M., Liao, Y., Zhu, S., Zhang, H., Pan, Z., Fang, P., Zeng, Z., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae006 · Cited by 34

Heat-Shock Proteins in Leukemia and Lymphoma: Multitargets for Innovative Therapeutic Approaches

Gibouin, V. C., Durand, M., Quéré, R., Girodon, F., Garrido, C., & Jégo, G. (2023) · Cancers · doi:10.3390/cancers15030984 · Cited by 33

CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma

Qi, L., Zhu, Y., Li, J., Zhou, M., Liu, B., Chen, J., & Shen, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-70208-w · Cited by 29

Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells

Isavand, P., Aghamiri, S. S., & Amin, R. (2024) · Biomedicines · doi:10.3390/biomedicines12081753 · Cited by 24

GC-CDSS: Personalized gastric cancer treatment recommendations system based on knowledge graph

Li, S., Li, Z., Xue, K., Zhou, X., Ding, C., Shao, Y., Zhang, S., Ruan, T., Zheng, M., & Sun, J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105402 · Cited by 23

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review

Mao, L., Wang, H., Hu, L., Tran, N. L., Canoll, P., Swanson, K. R., & Li, J. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3515839 · Cited by 25

Novel insights into HBV‐hepatocellular carcinoma at single‐cell sequencing

Yin, D., Zhou, T., Xia, X., Chang, H., Liu, Z., Li, Q., Shu, Y., & Xu, H. (2023) · MedComm – Oncology · doi:10.1002/mog2.60 · Cited by 23

How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data

Santangelo, G., Nicora, G., Bellazzi, R., & Dagliati, A. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02731-9 · Cited by 26

Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality

Krepper, D., Cesari, M., Hubel, N. J., Zelger, P., & Sztankay, M. J. (2024) · Journal of Patient-Reported Outcomes · doi:10.1186/s41687-024-00808-7 · Cited by 22

Multi-omics pan-cancer analyses identify MCM4 as a promising prognostic and diagnostic biomarker

Li, Y., Gao, W., Yang, Z., Hu, Z., & Li, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-57299-1 · Cited by 17

Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence

Derbal, Y. (2024) · Cancer Control · doi:10.1177/10732748241264704 · Cited by 17

Prediction of ECOG Performance Status of Lung Cancer Patients Using LIME-Based Machine Learning

Nguyen, H. V., & Byeon, H. (2023) · Mathematics · doi:10.3390/math11102354 · Cited by 15

From cancer big data to treatment: Artificial intelligence in cancer research

Danishuddin, M., Khan, S., & Kim, J.-J. (2023) · The Journal of Gene Medicine · doi:10.1002/jgm.3629 · Cited by 16

Computational model for disease research

Chen, X., & Li, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac615 · Cited by 16

Nano-enhanced photodynamic therapy and machine learning: Advancements, challenges, and future directions

Chota, A., Abrahamse, H., & George, B. P. A. (2025) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2025.118710 · Cited by 15

A Knowledge‐Guided Graph Learning Approach Bridging Phenotype‐ and Target‐Based Drug Discovery

Ye, Q., Zeng, Y., Jiang, L., Kang, Y., Pan, P., Chen, J., Deng, Y., Zhao, H., He, S., Hou, T., et al. (2025) · Advanced Science · doi:10.1002/advs.202412402 · Cited by 16

Expression And Prognostic Role of PRDX1 In Gastrointestinal Cancers

Zhang, Z., Zhou, P., Liu, M., & Pei, B. (2023) · Journal of Cancer · doi:10.7150/jca.86568 · Cited by 14

Overcoming Resistance to Standard-of-Care Therapies for Head and Neck Squamous Cell Carcinomas

Gauss, C., Stone, L. D., Ghafouri, M., Quan, D. L., Johnson, J., Fribley, A. M., & Amm, H. M. (2024) · Cells · doi:10.3390/cells13121018 · Cited by 14

Omics in acute‐on‐chronic liver failure

Li, P., Liang, X., Luo, J., & Li, J. (2023) · Liver International · doi:10.1111/liv.15634 · Cited by 15

Ten challenges and opportunities in computational immuno-oncology

Bao, R., Hutson, A. D., Madabhushi, A., Jönsson, V. D., Rosario, S. R., Barnholtz‐Sloan, J. S., Fertig, E. J., Marathe, H. G., Harris, L. N., Altreuter, J., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-009721 · Cited by 14

HEAL-KGGen: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Genetic Biomarker-Based Medical DiagnosisPreprint

Zuo, K., Zhong, Z., Huang, P., Tang, S., Chen, Y., & Jiang, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.03.657521 · Cited by 13

Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases

Alshorman, J., Mehran, M. J., Bahrami, Y., Mohammadzadeh, S., Barzigar, R., Morshedi, M., Haider, K. H., Tembo, K. M., Rong, S.-J., Jadgal, N., et al. (2026) · Clinical and Experimental Medicine · doi:10.1007/s10238-026-02107-5 · Cited by 15

Trans-activating mutations of the pseudokinase ERBB3

Koivu, M. K. A., Chakroborty, D., Airenne, T. T., Johnson, M. S., Kurppa, K. J., & Elenius, K. (2024) · Oncogene · doi:10.1038/s41388-024-03070-9 · Cited by 12

Real-World-Time Data and RCT Synergy: Advancing Personalized Medicine and Sarcoma Care through Digital Innovation

Heesen, P., Schelling, G., Birbaumer, M., Jäger, R., Bode, B., Studer, G., & Fuchs, B. (2024) · Cancers · doi:10.3390/cancers16142516 · Cited by 12

Advances and challenges in the origin and evolution of ovarian cancer organoids

Zhang, M., Yin, R., & Li, K. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1429141 · Cited by 12

New paradigms to break barriers in early cancer detection for improved prognosis and treatment outcomes

Ahmad, I., Jasim, S. A., Sharma, M. K., Renuka Jyothi, S., Hjazi, A. M., Mohammed, J. S., Sinha, A., Zwamel, A. H., Hamzah, H. F., & Mohammed, B. A. (2024) · The Journal of Gene Medicine · doi:10.1002/jgm.3730 · Cited by 12

Spatially Variable Genes

Biradar, S., Suresh, C., Nanjundeshwara, N., & Raghavan, R. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-7728-4.ch001 · Cited by 12

CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein

Ji, B., Hu, T., Wang, J., Liu, M., Xu, L., Zhang, Q., Zhong, S., Qiao, L., Zhang, Y., Peng, S., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72882-y · Cited by 14

Analytical strategies in early breast cancer diagnostic biomarker discovery by machine learning methods: Promises, advances and outlooks

Naghib, S. M., Khorasani, M. A., Sharifianjazi, F., & Tavamaishvili, K. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118412 · Cited by 11

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Wang, M., An, K., Huang, J., Mprah, R., & Ding, H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1193992 · Cited by 13

The combination of baseline neutrophil to lymphocyte ratio and dynamic changes during treatment can better predict the survival of osteosarcoma patients

Li, L., Li, Y., Lu, M., Wang, Y., Li, Z., Hu, X., He, X., Gong, T., Luo, Y., Zhou, Y., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1235158 · Cited by 12

Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning

Goldstein, Y., Cohen, O. T., Wald, O., Bavli, D., Kaplan, T., & Benny, O. (2024) · Science Advances · doi:10.1126/sciadv.adj4370 · Cited by 12

APOA1 mRNA and serum APOA1 protein as diagnostic and prognostic biomarkers in gastric cancer

Li, F., Han, M., Gao, X., Du, X., & Jiang, C. (2024) · Translational Cancer Research · doi:10.21037/tcr-23-1966 · Cited by 11

Computational modeling for medical data: From data collection to knowledge discovery

Yin, Y., Xu, S., Hong, Y., Cai, Y., Tang, W., Wang, J., Shen, B., Zong, H., & Yu, G. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100079 · Cited by 11

Editorial: Integrative analysis for complex disease biomarker discovery

Huang, H., Li, J., & Cho, W. C. S. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1273084 · Cited by 11

Photoinduced Transition from Quasi-Two-Dimensional Ruddlesden–Popper to Three-Dimensional Halide Perovskites for the Optical Writing of Multicolor and Light-Erasable Images

Anoshkin, S., Shishkin, I. I., Markina, D. I., Logunov, L., Demir, H. V., Rogach, A. L., Pushkarev, A. P., & Makarov, S. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c03151 · Cited by 10

Roadmap for a European cancer data management and precision medicine infrastructure

Nikolski, M., Hovig, E. J., Al‐Shahrour, F., Blomberg, N., Scollen, S., Valencia, A., & Saunders, G. I. (2024) · Nature Cancer · doi:10.1038/s43018-023-00717-6 · Cited by 11

Ethical implications of blockchain technology in biomedical research

Rubeis, G. (2024) · Ethik in der Medizin · doi:10.1007/s00481-024-00805-w · Cited by 10

Big data in breast cancer: Towards precision treatment

Zhang, H., Hussin, H., Hoh, C.-C., Cheong, S.-H., Lee, W.-K., & Yahaya, B. H. (2024) · Digital Health · doi:10.1177/20552076241293695 · Cited by 13

Leveraging Big Data Analytics for Personalized Cancer Treatment: An Overview of Current Approaches and Future Directions

Ahmed, M. K., Rozario, E., Mohonta, S. C., Ferdousmou, J., Saimon, A. S. M., Moniruzzaman, M., Manik, M. M. T. G., & Hasan, R. (2025) · Journal of Engineering · doi:10.1155/je/9928467 · Cited by 10

The Sarcoma-Specific Instrument to Longitudinally Assess Health-Related Outcomes of the Routine Care Cycle

Mosku, N., Heesen, P., Christen, S., Scaglioni, M. F., Bode, B., Studer, G., & Fuchs, B. (2023) · Diagnostics · doi:10.3390/diagnostics13061206 · Cited by 10

Integrative big transcriptomics data analysis implicates crucial role of MUC13 in pancreatic cancer

Dhasmana, A., Dhasmana, S., Agarwal, S., Khan, S., Haque, S., Jaggi, M., Yallapu, M. M., & Chauhan, S. C. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.04.029 · Cited by 10

BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning

Florentino, B. R., Bonidia, R. P., Sanches, N. H., da Rocha, U. N., & de Carvalho, A. C. P. L. F. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.031 · Cited by 9

AURKB/CDC37 complex promotes clear cell renal cell carcinoma progression via phosphorylating MYC and constituting an AURKB/E2F1-positive feedforward loop

Li, F., Wang, X., Zhang, J., Jing, X., Zhou, J. A., Jiang, Q., Cao, L. H., Cai, S., Miao, J., Tong, D., et al. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06827-y · Cited by 9

Toward Generative AI–Driven Metadata Modeling: A Human–Large Language Model Collaborative Approach

Bagchi, M. (2025) · Library trends · doi:10.1353/lib.2025.a961196 · Cited by 22

Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification

Peng, M.-H., Zhang, K., Guan, S.-W., Lin, Q. D., & Yu, H. (2025) · World Journal of Clinical Oncology · doi:10.5306/wjco.v16.i6.107646 · Cited by 9

Computational pathology in precision oncology: Evolution from task-specific models to foundation models

Wang, Y., Gu, Y., Zhang, X., Wang, B., Wang, R., Li, X., Liu, Y., Qu, F., Ren, F., Yan, R., et al. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003790 · Cited by 9

Construction of novel hypoxia-related gene model for prognosis and tumor microenvironment in endometrial carcinoma

Chen, J., Wang, G., Zhang, D., Luo, X., Zhang, D., & Zhang, Y. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1075431 · Cited by 9

Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in OncologyPreprint

Waqas, A., Tripathi, A. G., Ahmed, S., Mukund, A., Farooq, H., Johnson, J., Stewart, P., Naeini, M., Schabath, M. B., & Rasool, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5055163 · Cited by 16

DNMT3L inhibits hepatocellular carcinoma progression through DNA methylation of CDO1: insights from big data to basic research

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Integrated machine learning-driven disulfidptosis profiling: CYFIP1 and EMILIN1 as therapeutic nodes in neuroblastoma

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