Gut microbiome, big data and machine learning to promote precision medicine for cancer

Search and clustering orders of magnitude faster than BLAST

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Metagenomic biomarker discovery and explanation

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The UK Biobank resource with deep phenotyping and genomic data

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The human microbiome project

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Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors

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Diversity, stability and resilience of the human gut microbiota

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Role of the microbiota in immunity and inflammation.

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Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients

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Machine Learning in Medicine

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Helicobacter pylori Infection and the Risk of Gastric Carcinoma

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Gut microbiome engineering with probiotics: current trends and future directions

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Performance of Lung Cancer Prediction Methods Using Different Classification Algorithms

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Metabolic modeling of synthetic microbial communities for bioremediation

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The Interaction among Microbiota, Epigenetic Regulation, and Air Pollutants in Disease Prevention

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Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models

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Oropharyngeal microbiome profiled at admission is predictive of the need for respiratory support among COVID-19 patients

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Enhanced Metagenomic Deep Learning for Disease Prediction and Reproducible Signature Identification by Restructured Microbiome 2D-Representations

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Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective

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A Comprehensive Oncological Biomarker Framework Guiding Precision Medicine

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The new microbiology: cultivating the future of microbiome-directed medicine

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A neural network‐based framework to understand the type 2 diabetes‐related alteration of the human gut microbiome

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The influence of machine learning technologies in gut microbiome research and cancer studies - A review

Loganathan, T., & Doss, C. G. P. (2022) · Life Sciences · doi:10.1016/j.lfs.2022.121118 · Cited by 20

Combined Use of Immune Checkpoint Inhibitors and Phytochemicals as a Novel Therapeutic Strategy against Cancer

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iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records

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DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy

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Machine learning in metastatic cancer research: Potentials, possibilities, and prospects

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Exploring the human microbiome – A step forward for precision medicine in breast cancer

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Role of circular RNAs and gut microbiome in gastrointestinal cancers and therapeutic targets

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Artificial Intelligence and Precision Medicine: A Perspective

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Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses

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Artificial intelligence for microbiology and microbiome research

Wang, X.-W., Wang, T. D., & Liu, Y. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101531 · Cited by 24

A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning

Wang, Z., Xu, C., Liu, W., Zhang, M., Zou, J., Shao, M., Feng, X., Yang, Q., Li, W., Shi, X., et al. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1083569 · Cited by 18

Diagnosis of Fibrosis Using Blood Markers and Logistic Regression in Southeast Asian Patients With Non-alcoholic Fatty Liver Disease

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A Machine Learning Approach Reveals a Microbiota Signature for Infection with Mycobacterium avium subsp. paratuberculosis in Cattle

Lee, S., Park, H.-T., Park, S., Lee, J. H., Kim, D., Yoo, H. S., & Kim, D. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.03134-22 · Cited by 16

Harnessing human microbiomes for disease prediction

Liu, Y., Fachrul, M., Inouye, M., & Méric, G. (2024) · Trends in Microbiology · doi:10.1016/j.tim.2023.12.004 · Cited by 18

Approaching precision medicine by tailoring the microbiota

Ryu, G., Kim, H., & Koh, A. (2021) · Mammalian Genome · doi:10.1007/s00335-021-09859-3 · Cited by 17

Machine learning on microbiome research in gastrointestinal cancer

Cheung, H., & Yu, J. Q. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15502 · Cited by 16

Building up a clinical microbiota profiling: a quality framework proposal

Scherz, V., Greub, G., & Bertelli, C. (2021) · Critical Reviews in Microbiology · doi:10.1080/1040841x.2021.1975642 · Cited by 19

Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools

Khelfaoui, I., Wang, W., Meskher, H., Shehata, A. I., Basuini, M. F. E., Abouelenein, M. F., Degha, H. E., Alhoshy, M., Teiba, I. I., & Mahmoud, S. S. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1691503 · Cited by 16

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

Abedi, V., Razavi, S., Khan, A., Avula, V., Tompe, A. P., Poursoroush, A., Sadr, A. V., Li, J., & Zand, R. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10235710 · Cited by 17

Deviations from optimal storage temperature and its impact on postharvest quality of table grape cv. Scarlotta Seedless

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Critical assessment of machine learning approaches for classification, dynamic prediction and surrogate Modeling in food fermentation

Campo-Manzanares, N., Moimenta, A. R., & Balsa‐Canto, E. (2026) · Food Research International · doi:10.1016/j.foodres.2026.118403 · Cited by 14

Supervised learning and model analysis with compositional data

Huang, S., Ailer, E., Kilbertus, N., & Pfister, N. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011240 · Cited by 18

Crohn’s Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome

Ünal, M., Bostancı, E., Özkul, C., Açıcı, K., Aşuroğlu, T., & Güzel, M. S. (2023) · Diagnostics · doi:10.3390/diagnostics13172835 · Cited by 13

An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series

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Human Gut Microbiota in Heart Failure: Trying to Unmask an Emerging Organ

PARASKEVAIDIS, I. A., Xanthopoulos, A. V., Tsougos, E., & Triposkiadis, F. K. (2023) · Biomedicines · doi:10.3390/biomedicines11092574 · Cited by 13

Gut microbiota in gastric cancer: from pathogenesis to precision medicine

Huo, S., Lv, K., Han, L., Zhao, Y., & Jiang, J. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1606924 · Cited by 13

Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review

Zhu, Y., Tang, Y., Qi, X., & Zhu, X. (2026) · Bioengineering · doi:10.3390/bioengineering13020144 · Cited by 15

Dual contribution of the gut microbiome to immunotherapy efficacy and toxicity: supportive care implications and recommendations

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Toward Multilabel Classification for Multiple Disease Prediction Using Gut Microbiota Profiles

Huang, Z.-A., Hu, P., Hu, L., You, Z.-H., Tan, K. C., & Huang, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3453967 · Cited by 13

The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling

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Review of Personalized Cancer Treatment with Machine Learning

Ahmed, H., Hamad, S. H., Shedeed, H. A., & Saad, A. (2022) · International Conference on Computing and Informatics (ICCI) · doi:10.1109/icci54321.2022.9756124 · Cited by 13

The application of machine learning in clinical microbiology and infectious diseases

Cheng, X., Zhao, L., Ye, C.-S., Xu, K.-C., & Xu, K. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1545646 · Cited by 18

Predictive modeling of colorectal cancer using exhaustive analysis of microbiome information layers available from public metagenomic data

Murovec, B., Deutsch, L., & Stres, B. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1426407 · Cited by 14

Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

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Evaluation of the Storage Performance of ‘Valencia’ Oranges and Generation of Shelf-Life Prediction Models

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Fecal microbiota transplantation: a review on current formulations inClostridioides difficileinfection and future outlooks

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Interlayer-enhanced magnetoresistance and electroresistance in V Se 2 / Sc 2 CO 2 / Ni Cl Br / V Se 2 van der Waals multiferroic tunnel junctions

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Incorporating Artificial Intelligence (AI) for Precision Medicine

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PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network

Wang, Q., Fan, X., Wu, S., & Su, X. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae013 · Cited by 13

Multi-omics analysis of immune-related microbiome and prognostic model in head and neck squamous cell carcinoma

Liu, Y., Lin, H., Zhong, W., Zeng, Y., Zhou, G., Chen, Z., Huang, S., Zhang, L., & Liu, X. (2024) · Clinical Oral Investigations · doi:10.1007/s00784-024-05645-y · Cited by 11

Exploring the effects of gut microbiota on cholangiocarcinoma progression by patient-derived organoids

Lederer, A., Görrissen, N., Nguyen, T. T., Kreutz, C., Rasel, H., Bartsch, F., Lang, H., & Endres, K. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-024-06012-x · Cited by 11

GC–MS urinary metabolomics analysis of inherited metabolic diseases and stable metabolic biomarker screening by a comprehensive chemometric method

Yang, Q., Shi, B.-H., Tian, G.-L., Niu, Q.-Q., Tang, J., Linghu, D.-D., He, H.-Q., Wu, B., Yang, J., Xu, L., et al. (2021) · Microchemical Journal · doi:10.1016/j.microc.2021.106350 · Cited by 11

Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance

Iqbal, M. A., Siddiqui, S., Smith, K., Singh, P. K., Kumar, B., Chouaı̈b, S., & Chandrasekaran, S. (2023) · iScience · doi:10.1016/j.isci.2023.108059 · Cited by 13

Intestinal microbiota alterations by dietary exposure to chemicals from food cooking and processing. Application of data science for risk prediction

Ruiz-Saavedra, S., García-González, H., Arboleya, S., Salazar, N., Gayo, J. E. L., Dı́az, I., Gueimonde, M., González, S., & Reyes‐Gavilán, C. G. D. L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.037 · Cited by 11

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

Applications of machine learning in tumor-associated macrophages

Li, Z., Yu, Q., Zhu, Q., Yang, X., Li, Z., & Fu, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.985863 · Cited by 10

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease

Philip, D., Hodgkiss, R., Radhakrishnan, S. K., Sinha, A., & Acharjee, A. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06552-w · Cited by 13

MicrobioSee: A Web-Based Visualization Toolkit for Multi-Omics of Microbiology

Li, J., Sang, Y., Zeng, S., Mo, S., Zhang, Z., He, S., Li, X., Su, G., Liao, J., & Jiang, C. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.853612 · Cited by 9

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

Bonomi, F., Peretti, S., Lepri, G., Venerito, V., Russo, E., Bruni, C., Iannone, F., Tangaro, S., Amedei, A., Guiducci, S., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081198 · Cited by 10

Microbiome and its implications in oncogenesis: a Mendelian randomization perspective.

Feng, K., Ren, F., Xing, Z., Zhao, Y., Yang, C., Liu, J., Shang, Q., Wang, X., & Wang, X. (2023) · PubMed Central · Cited by 11

A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics

Sriraman, H., Saleena, B., Vats, S., & Balasubramanian, P. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s446745 · Cited by 12

Immunotherapy in biliary tract cancer: reshaping the tumour microenvironment and advancing precision combination strategies

Xue, J., Zhang, L., Zhang, K., Zhou, K., & Zhao, H. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1651769 · Cited by 9

Intratumorally specific microbial-derived lipopolysaccharide contributes to non-small cell lung cancer progression

Sha, G., Wu, Z., Wang, B., Ding, Y., Xiao, Z., Zhang, W., Zhou, J., Zhou, Y., Ji, G., Tian, Z., et al. (2025) · Virulence · doi:10.1080/21505594.2025.2548626 · Cited by 11

Optimizing breast cancer chemotherapy by harnessing gut microbiota with insights from artificial intelligence

Zhou, Y., Jiang, M., Shen, K., He, T., Zhou, S., Shao, A., Li, X., Wang, J., Wu, R., Singla, R. K., et al. (2025) · npj Biofilms and Microbiomes · doi:10.1038/s41522-025-00825-4 · Cited by 9

Metagenomics studies for the diagnosis and treatment of prostate cancer

Zarei, A., Javid, H., Sanjarian, S., Senemar, S., & Zarei, H. (2021) · The Prostate · doi:10.1002/pros.24276 · Cited by 9

The Potential of Naturally Derived Compounds for Treating Chronic Kidney Disease: A Review of Autophagy and Cellular Senescence

Teh, Y. M., Mualif, S. A., Noh, N. I. M., & Lim, S. K. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms26010003 · Cited by 8

Impact of diet and gut microbiota changes in the development of hepatocellular carcinoma

Scarlata, G. G. M., Cicino, C., Spagnuolo, R., Marascio, N., Quirino, A., Matera, G., Dumitrascu, D. L., Luzza, F., & Abenavoli, L. (2024) · Hepatoma Research · doi:10.20517/2394-5079.2023.122 · Cited by 8

DDLA: a double deep latent autoencoder for diabetic retinopathy diagnose based on continuous glucose sensors

Tao, R., Li, H., Lu, J., Huang, Y., Wang, Y., Lu, W., Shao, X., Zhou, J., & Yu, X. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03120-0 · Cited by 8

Targeting microbiome, drug metabolism, and drug delivery in oncology

Sumiyoshi, A., Fujii, H., & Okuma, Y. (2023) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2023.114902 · Cited by 8

Artificial Intelligence and Machine Learning in Precision Health: An Overview of Methods, Challenges, and Future Directions

Bennett, R., Hemmati, M., Ramesh, R., & Razzaghi, T. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-74006-0_2 · Cited by 8

Culturomics: Deciphering the Microbial Dark Matter in the Oral Cavity

Bartsch, S., Kreutz, C., Scholz, K. J., Kohnert, E., Hertel, J., Wolf, M., Jakubovics, N. S., Al‐Ahmad, A., & Cieplik, F. (2025) · Journal of Dental Research · doi:10.1177/00220345251346781 · Cited by 7

XGBoost-based urinary microbial signatures enable non-invasive diagnosis and prognosis for urothelial carcinoma

Xie, H., Dong, C., Li, Y., Guo, J., Yang, Y., Yang, J., Li, X., Cha, J., Hu, S., & Lin, C. (2025) · BMC Microbiology · doi:10.1186/s12866-025-04455-w · Cited by 7

High-resolution soil salinity mapping and driving factor analysis at regional scale using multi-source remote sensing data

Liu, Y., Zhu, Y., Qian, Y., Xu, W., Wei, G., & Huang, J. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.134604 · Cited by 7

Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto’s thyroiditis through bioinformatics analysis and machine learning

Jiang, H., He, Y., Lan, X., & Xie, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-66162-2 · Cited by 8

A bibliographic outlook: machine learning on biofilm

Ding, Y., & Chen, S. (2024) · Research Directions Biotechnology Design · doi:10.1017/btd.2024.28 · Cited by 7

Regulating gut microbiota with nanomaterials for cancer immunotherapy

Sha, T., Tang, Y., Hu, X., Zhou, H., & Rao, L. (2025) · Interdisciplinary medicine · doi:10.1002/inmd.20240103 · Cited by 6

Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective

Seo, H., Kwon, C.-O., Park, J., Kang, C.-S., Shin, T.-S., Yang, E.-Y., Jung, J. W., Moon, B., & Kim, Y. (2022) · Microorganisms · doi:10.3390/microorganisms10091833 · Cited by 7

Autoencoder-assisted latent representation learning for survival prediction and multi-view clustering on multi-omics cancer subtyping

Zhu, S., Wang, W., Fang, W. H., & Cui, M. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023933 · Cited by 7

Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses

Guo, Y., Li, S., Li, C., Wang, L., & Ning, W. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1326018 · Cited by 6

Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model

Zhou, D., Chen, Y., Wang, Z., Zhu, S., Zhang, L., Song, J., Bai, T., & Hou, X. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1298679 · Cited by 6

Informatics for your Gut: at the Interface of Nutrition, the Microbiome, and Technology

Cooper, K., Clarke, M. A., & Clayton, J. B. (2023) · Yearbook of Medical Informatics · doi:10.1055/s-0043-1768723 · Cited by 6

Development of colorectal cancer detection and prediction based on gut microbiome big-data

Sun, Y., Fan, X., & Zhao, J. (2022) · Medicine in Microecology · doi:10.1016/j.medmic.2022.100053 · Cited by 6

Quantum Leap: Is the Conflux of Artificial Intelligence, Personalized Medicine, Nutrigenetics and Nutrigenomics the Path to Optimal Health?

Asediya, V., & Anjaria, P. A. (2023) · Epigenomics · doi:10.2217/epi-2023-0084 · Cited by 6

Phage therapy in oncology: opportunities for cancer prevention and treatment

Mutalik, V. K., Inman, J. L., Chang, H., Arkin, A., & Mao, J.-H. (2026) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2026.02.001 · Cited by 7

Nutritional Risk Prediction based on Random Forest Classifier

Shi, S., & Zhang, H. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934612 · Cited by 5

Gut‑lung axis microbiome: Towards precision medicine in respiratory disorders (Review)

Manoharan, S., Oviya, I. R., Prabahar, A., Bhasuran, B., & Raja, K. (2025) · World Academy of Sciences Journal · doi:10.3892/wasj.2025.376 · Cited by 6

MV-CVIB: a microbiome-based multi-view convolutional variational information bottleneck for predicting metastatic colorectal cancer

Cui, Z., Wu, Y. X., Zhang, Q., Wang, S., He, Y., & Huang, D.-S. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1238199 · Cited by 5

Emerging Microbial Technologies

Prasad, M., Brar, B., Bala, K., & Singh, N. (2023) · Indian Journal of Microbiology · doi:10.1007/s12088-023-01103-7 · Cited by 5

Microbiome-Host Interactions: The Role of Gut Bacteria in Neurological Disorders

Nasir, O., Bibi, R., Qamar, M. U., Fatima, A., Ahsan, Z., Khan, F., & Amjad, S. (2023) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.4.1023.102705 · Cited by 5

Construction and application of machine learning models for predicting intradialytic hypotension

Wang, P., Xu, N., Wu, L., Hong, Y., Qu, Y., Ren, Z., Luo, Q., & Cai, K. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333357 · Cited by 5

Using machine learning and clinical registry data to uncover variation in clinical decision making

James, C. R., Allen, M. J., James, M. A., & Everson, R. (2023) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2023.100098 · Cited by 5

Deep Learning Models for Early Diagnosis of Pancreatic Cancer

Geetha, R., Vidhya, S., Rani, P. J. I., MeenaAbarna, K. T., Sudhaakar, K., & Mohan, E. (2023) · International Conference on Computation, Automation and Knowledge Management (ICCAKM) · doi:10.1109/iccakm58659.2023.10449518 · Cited by 5

Appendix removal affects the subsequent cancer risk in Asian adults: A territory-wide population-based cohort study

Hu, C., Guo, C. L., Lau, H. C.-H., Shi, F., Zhang, Z., Guo, G., Liu, G., Chen, Y., Lau, L. H. S., Zhang, L., et al. (2024) · Cancer Letters · doi:10.1016/j.canlet.2024.217087 · Cited by 5

Machine learning on the road to unlocking microbiota’s potential for boosting immune checkpoint therapy

Wojciechowski, S., Majchrzak-Gorecka, M., Biernat, P., Odrzywołek, K., Pruss, Ł., Zych, K., Majta, J., & Milanowska, K. (2022) · International Journal of Medical Microbiology · doi:10.1016/j.ijmm.2022.151560 · Cited by 5

Oncological big data platforms for promoting digital competencies and professionalism in Chinese medical students: a cross-sectional study

Liu, J., Jiao, X., Zeng, S., Li, H., Jin, P., Chi, J., Liu, X., Yang, Y., Ma, G., Zhao, Y., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-061015 · Cited by 5

Personalized medicine

Bastaki, K., Umlai, U. I., & Jithesh, P. V. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99924-3.00004-2 · Cited by 5

Detection of Merkel cell polyomavirus in multiple primary oral squamous cell carcinomas

Kitamura, N., Hashida, Y., Higuchi, T., Ohno, S., Sento, S., Sasabe, E., Murakami, I., Yamamoto, T., & Daibata, M. (2023) · Odontology · doi:10.1007/s10266-023-00807-y · Cited by 5

Anesthesia decision analysis using a cloud-based big data platform

Zhang, S., Li, H., Jing, Q., Shen, W., Luo, W., & Dai, R. (2024) · European journal of medical research · doi:10.1186/s40001-024-01764-0 · Cited by 5

Downregulation of ABLIM3 confers to the metastasis of neuroblastoma via regulating the cell adhesion molecules pathway

Gong, B., Qu, T., Zhang, J., Jia, Y., Song, Z., Chen, C., Yang, J., Wang, C., Liu, Y., Jin, Y., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.024 · Cited by 6

The influence of machine learning technologies in gut microbiome research and cancer studies - A review

Loganathan, T., & Doss, C. G. P. (2022) · Life Sciences · doi:10.1016/j.lfs.2022.121118 · Cited by 20

Gut Microbiome and Precision Medicine in Cancer

Mbodi, L., Chatziioannou, A. A., & Dlamini, Z. (2024) · CRC Press eBooks · doi:10.1201/9781032706450-14

Host Microbiomes in Tumor Precision Medicine: How far are we?

D’Amico, F., Barone, M., Tavella, T., Rampelli, S., Brigidi, P., & Turroni, S. (2022) · Current Medicinal Chemistry · doi:10.2174/0929867329666220105121754 · Cited by 16

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Feng, J., Yang, K., Liu, X., Song, M. S., Zhan, P., Zhang, M., Chen, J., & Liu, J. (2023) · PeerJ · doi:10.7717/peerj.16304 · Cited by 4

Artificial Intelligence Application to Microbiomics Data for Improved Clinical Decision Making in Precision Oncology

Mbatha, S. Z., Mkhize‐Kwitshana, Z. L., Mulaudzi, T. V., Hull, R., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_8 · Cited by 1