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For: Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ, Gasbarrini A, Tortora G. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nat Rev Gastroenterol Hepatol 2020;17:635-48. [DOI: 10.1038/s41575-020-0327-3] [Cited by in Crossref: 76] [Cited by in F6Publishing: 64] [Article Influence: 38.0] [Reference Citation Analysis]
Number Citing Articles
1 Li Z, Yu Q, Zhu Q, Yang X, Li Z, Fu J. Applications of machine learning in tumor-associated macrophages. Front Immunol 2022;13:985863. [DOI: 10.3389/fimmu.2022.985863] [Reference Citation Analysis]
2 Seo H, Kwon C, Park J, Kang C, Shin T, Yang E, Jung JW, Moon B, Kim Y. 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. Microorganisms 2022;10:1833. [DOI: 10.3390/microorganisms10091833] [Reference Citation Analysis]
3 Li X, Chen Z, Lin J, Wang S, Song C, Abualigah L. Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach. Computational and Mathematical Methods in Medicine 2022;2022:1-19. [DOI: 10.1155/2022/4862376] [Reference Citation Analysis]
4 Cai H, Li M, Deng R, Wang M, Shi Y. Advances in molecular biomarkers research and clinical application progress for gastric cancer immunotherapy. Biomark Res 2022;10:67. [PMID: 36042469 DOI: 10.1186/s40364-022-00413-0] [Reference Citation Analysis]
5 Rahmani Dabbagh S, Ozcan O, Tasoglu S. Machine learning-enabled optimization of extrusion-based 3D printing. Methods 2022;206:27-40. [PMID: 35963502 DOI: 10.1016/j.ymeth.2022.08.002] [Reference Citation Analysis]
6 Abdelhalim H, Berber A, Lodi M, Jain R, Nair A, Pappu A, Patel K, Venkat V, Venkatesan C, Wable R, Dinatale M, Fu A, Iyer V, Kalove I, Kleyman M, Koutsoutis J, Menna D, Paliwal M, Patel N, Patel T, Rafique Z, Samadi R, Varadhan R, Bolla S, Vadapalli S, Ahmed Z. Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine. Front Genet 2022;13:929736. [PMID: 35873469 DOI: 10.3389/fgene.2022.929736] [Reference Citation Analysis]
7 Rakotonirina A, Galperine T, Allémann E. Fecal microbiota transplantation: a review on current formulations in Clostridioides difficile infection and future outlooks. Expert Opin Biol Ther 2022. [PMID: 35763604 DOI: 10.1080/14712598.2022.2095901] [Reference Citation Analysis]
8 Wu X, Zhou Q, Mu L, Hu X. Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives. J Hazard Mater 2022;438:129487. [PMID: 35816807 DOI: 10.1016/j.jhazmat.2022.129487] [Reference Citation Analysis]
9 Owoyemi A, Porat R, Lichter A, Doron-faigenboim A, Jovani O, Koenigstein N, Salzer Y. Large-Scale, High-Throughput Phenotyping of the Postharvest Storage Performance of ‘Rustenburg’ Navel Oranges and the Development of Shelf-Life Prediction Models. Foods 2022;11:1840. [DOI: 10.3390/foods11131840] [Reference Citation Analysis]
10 Laccourreye P, Bielza C, Larrañaga P. Explainable Machine Learning for Longitudinal Multi-Omic Microbiome. Mathematics 2022;10:1994. [DOI: 10.3390/math10121994] [Reference Citation Analysis]
11 Hasanzad M, Sarhangi N, Ehsani Chimeh S, Ayati N, Afzali M, Khatami F, Nikfar S, Aghaei Meybodi HR. Precision medicine journey through omics approach. J Diabetes Metab Disord 2022;21:881-8. [PMID: 35673436 DOI: 10.1007/s40200-021-00913-0] [Reference Citation Analysis]
12 Aziz RM. Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data. Med Biol Eng Comput 2022;60:1627-46. [PMID: 35399141 DOI: 10.1007/s11517-022-02555-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
13 Luo W, Guo S, Zhou Y, Zhao J, Wang M, Sang L, Chang B, Wang B. Hepatocellular Carcinoma: How the Gut Microbiota Contributes to Pathogenesis, Diagnosis, and Therapy. Front Microbiol 2022;13:873160. [PMID: 35572649 DOI: 10.3389/fmicb.2022.873160] [Reference Citation Analysis]
14 Aziz RM. Cuckoo Search-Based Optimization for Cancer Classification: A New Hybrid Approach. Journal of Computational Biology. [DOI: 10.1089/cmb.2021.0410] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Ianiro G, Iorio A, Porcari S, Masucci L, Sanguinetti M, Perno CF, Gasbarrini A, Putignani L, Cammarota G. How the gut parasitome affects human health. Therap Adv Gastroenterol 2022;15:175628482210915. [DOI: 10.1177/17562848221091524] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Park EM, Chelvanambi M, Bhutiani N, Kroemer G, Zitvogel L, Wargo JA. Targeting the gut and tumor microbiota in cancer. Nat Med 2022;28:690-703. [PMID: 35440726 DOI: 10.1038/s41591-022-01779-2] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 9.0] [Reference Citation Analysis]
17 Cekikj M, Jakimovska Özdemir M, Kalajdzhiski S, Özcan O, Sezerman OU. Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models. Applied Sciences 2022;12:4094. [DOI: 10.3390/app12094094] [Reference Citation Analysis]
18 Li Q, Yang H, Wang P, Liu X, Lv K, Ye M. XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer. J Transl Med 2022;20. [DOI: 10.1186/s12967-022-03369-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Feng S, Meng C, Hao Z, Liu H. Bacillus licheniformis Reshapes the Gut Microbiota to Alleviate the Subhealth. Nutrients 2022;14:1642. [PMID: 35458204 DOI: 10.3390/nu14081642] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
20 Ghaffari P, Shoaie S, Nielsen LK. Irritable bowel syndrome and microbiome; Switching from conventional diagnosis and therapies to personalized interventions. J Transl Med 2022;20. [DOI: 10.1186/s12967-022-03365-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
21 Li J, Sang Y, Zeng S, Mo S, Zhang Z, He S, Li X, Su G, Liao J, Jiang C. MicrobioSee: A Web-Based Visualization Toolkit for Multi-Omics of Microbiology. Front Genet 2022;13:853612. [DOI: 10.3389/fgene.2022.853612] [Reference Citation Analysis]
22 Wardill HR, Chan RJ, Chan A, Keefe D, Costello SP, Hart NH. Dual contribution of the gut microbiome to immunotherapy efficacy and toxicity: supportive care implications and recommendations. Support Care Cancer 2022. [PMID: 35266052 DOI: 10.1007/s00520-022-06948-0] [Reference Citation Analysis]
23 Chiu HY, Chao HS, Chen YM. Application of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2022;14:1370. [PMID: 35326521 DOI: 10.3390/cancers14061370] [Reference Citation Analysis]
24 Chen Y, Su X. Search-based health status detection and disease classification using species-level profiles of metagenomes. Medicine in Microecology 2022;11:100048. [DOI: 10.1016/j.medmic.2021.100048] [Reference Citation Analysis]
25 McCoubrey LE, Seegobin N, Elbadawi M, Hu Y, Orlu M, Gaisford S, Basit AW. Active Machine learning for formulation of precision probiotics. Int J Pharm 2022;616:121568. [PMID: 35150845 DOI: 10.1016/j.ijpharm.2022.121568] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
26 Mccoubrey LE, Elbadawi M, Basit AW. Current clinical translation of microbiome medicines. Trends in Pharmacological Sciences 2022. [DOI: 10.1016/j.tips.2022.02.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
27 Chen Y, Wang H, Lu W, Wu T, Yuan W, Zhu J, Lee YK, Zhao J, Zhang H, Chen W. Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning. Gut Microbes 2022;14:2025016. [PMID: 35040752 DOI: 10.1080/19490976.2021.2025016] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
28 Pulliero A, Traversi D, Franchitti E, Barchitta M, Izzotti A, Agodi A. The Interaction among Microbiota, Epigenetic Regulation, and Air Pollutants in Disease Prevention. J Pers Med 2021;12:14. [PMID: 35055330 DOI: 10.3390/jpm12010014] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
29 Wang X, Xiao Y, Xu X, Guo L, Yu Y, Li N, Xu C. Characteristics of Fecal Microbiota and Machine Learning Strategy for Fecal Invasive Biomarkers in Pediatric Inflammatory Bowel Disease. Front Cell Infect Microbiol 2021;11:711884. [PMID: 34950604 DOI: 10.3389/fcimb.2021.711884] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
30 Narayana JK, Mac Aogáin M, Goh WWB, Xia K, Tsaneva-Atanasova K, Chotirmall SH. Mathematical-based microbiome analytics for clinical translation. Comput Struct Biotechnol J 2021;19:6272-81. [PMID: 34900137 DOI: 10.1016/j.csbj.2021.11.029] [Reference Citation Analysis]
31 Abedi V, Razavi SM, Khan A, Avula V, Tompe A, Poursoroush A, Vafaei Sadr A, Li J, Zand R. Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine. J Clin Med 2021;10:5710. [PMID: 34884412 DOI: 10.3390/jcm10235710] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
32 Zarei A, Javid H, Sanjarian S, Senemar S, Zarei H. Metagenomics studies for the diagnosis and treatment of prostate cancer. Prostate 2021. [PMID: 34855234 DOI: 10.1002/pros.24276] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
33 Scherz V, Greub G, Bertelli C. Building up a clinical microbiota profiling: a quality framework proposal. Crit Rev Microbiol 2021;:1-20. [PMID: 34752719 DOI: 10.1080/1040841X.2021.1975642] [Reference Citation Analysis]
34 Díez López C, Vidaki A, Kayser M. Integrating the human microbiome in the forensic toolkit: Current bottlenecks and future solutions. Forensic Sci Int Genet 2021;56:102627. [PMID: 34742094 DOI: 10.1016/j.fsigen.2021.102627] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
35 Zhang L, Zhong Q, Yu Z. Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm. Comput Intell Neurosci 2021;2021:8996673. [PMID: 34712319 DOI: 10.1155/2021/8996673] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
36 Firoozbakht F, Yousefi B, Schwikowski B. An overview of machine learning methods for monotherapy drug response prediction. Brief Bioinform 2021:bbab408. [PMID: 34619752 DOI: 10.1093/bib/bbab408] [Reference Citation Analysis]
37 Zhang Q, Zhang Z, Lu T, Yu Y, Penuelas J, Zhu YG, Qian H. Gammaproteobacteria, a core taxon in the guts of soil fauna, are potential responders to environmental concentrations of soil pollutants. Microbiome 2021;9:196. [PMID: 34593032 DOI: 10.1186/s40168-021-01150-6] [Cited by in F6Publishing: 6] [Reference Citation Analysis]
38 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Cited by in F6Publishing: 20] [Reference Citation Analysis]
39 Mendoza-Suárez M, Andersen SU, Poole PS, Sánchez-Cañizares C. Competition, Nodule Occupancy, and Persistence of Inoculant Strains: Key Factors in the Rhizobium-Legume Symbioses. Front Plant Sci 2021;12:690567. [PMID: 34489993 DOI: 10.3389/fpls.2021.690567] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
40 Gordon-Rodriguez E, Quinn TP, Cunningham JP. Learning Sparse Log-Ratios for High-Throughput Sequencing Data. Bioinformatics 2021:btab645. [PMID: 34498030 DOI: 10.1093/bioinformatics/btab645] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
41 Ansari AF, Reddy YBS, Raut J, Dixit NM. An efficient and scalable top-down method for predicting structures of microbial communities. Nat Comput Sci 2021;1:619-28. [DOI: 10.1038/s43588-021-00131-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
42 Su X. Elucidating the Beta-Diversity of the Microbiome: from Global Alignment to Local Alignment. mSystems 2021;6:e0036321. [PMID: 34402645 DOI: 10.1128/mSystems.00363-21] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
43 McCoubrey LE, Gaisford S, Orlu M, Basit AW. Predicting drug-microbiome interactions with machine learning. Biotechnol Adv 2021;:107797. [PMID: 34260950 DOI: 10.1016/j.biotechadv.2021.107797] [Cited by in Crossref: 14] [Cited by in F6Publishing: 4] [Article Influence: 14.0] [Reference Citation Analysis]
44 Bellando-Randone S, Russo E, Venerito V, Matucci-Cerinic M, Iannone F, Tangaro S, Amedei A. Exploring the Oral Microbiome in Rheumatic Diseases, State of Art and Future Prospective in Personalized Medicine with an AI Approach. J Pers Med 2021;11:625. [PMID: 34209167 DOI: 10.3390/jpm11070625] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
45 Gu S, Lv L, Wu Z, Li L. Reply to Klann et al. Clin Infect Dis 2021;72:2248-9. [PMID: 32780829 DOI: 10.1093/cid/ciaa1194] [Reference Citation Analysis]
46 Wang Q, Liu Z, Yan B, Chou WC, Ettwiller L, Ma Q, Liu B. A novel computational framework for genome-scale alternative transcription units prediction. Brief Bioinform 2021:bbab162. [PMID: 33957668 DOI: 10.1093/bib/bbab162] [Reference Citation Analysis]
47 Wu S, Chen Y, Li Z, Li J, Zhao F, Su X. Towards multi-label classification: Next step of machine learning for microbiome research. Comput Struct Biotechnol J 2021;19:2742-9. [PMID: 34093989 DOI: 10.1016/j.csbj.2021.04.054] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
48 Zhang Z, Femi-oyetoro J, Fidan I, Ismail M, Allen M. Prediction of Dimensional Changes of Low-Cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques. Metals 2021;11:690. [DOI: 10.3390/met11050690] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
49 Zeng T, Yu X, Chen Z. Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review. J Gastroenterol Hepatol 2021;36:832-40. [PMID: 33880762 DOI: 10.1111/jgh.15503] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
50 Cheung H, Yu J. Machine learning on microbiome research in gastrointestinal cancer. J Gastroenterol Hepatol 2021;36:817-22. [PMID: 33880761 DOI: 10.1111/jgh.15502] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
51 Rodriguez-Arrastia M, Martinez-Ortigosa A, Rueda-Ruzafa L, Folch Ayora A, Ropero-Padilla C. Probiotic Supplements on Oncology Patients' Treatment-Related Side Effects: A Systematic Review of Randomized Controlled Trials. Int J Environ Res Public Health 2021;18:4265. [PMID: 33920572 DOI: 10.3390/ijerph18084265] [Cited by in Crossref: 2] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
52 De Maria Marchiano R, Di Sante G, Piro G, Carbone C, Tortora G, Boldrini L, Pietragalla A, Daniele G, Tredicine M, Cesario A, Valentini V, Gallo D, Babini G, D'Oria M, Scambia G. Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go. J Pers Med 2021;11:216. [PMID: 33803592 DOI: 10.3390/jpm11030216] [Cited by in Crossref: 4] [Cited by in F6Publishing: 18] [Article Influence: 4.0] [Reference Citation Analysis]
53 Mollaei M, Hassan ZM, Khorshidi F, Langroudi L. Chemotherapeutic drugs: Cell death- and resistance-related signaling pathways. Are they really as smart as the tumor cells? Transl Oncol 2021;14:101056. [PMID: 33684837 DOI: 10.1016/j.tranon.2021.101056] [Cited by in Crossref: 1] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
54 Ryu G, Kim H, Koh A. Approaching precision medicine by tailoring the microbiota. Mamm Genome 2021;32:206-22. [PMID: 33646347 DOI: 10.1007/s00335-021-09859-3] [Reference Citation Analysis]
55 McCoubrey LE, Elbadawi M, Orlu M, Gaisford S, Basit AW. Harnessing machine learning for development of microbiome therapeutics. Gut Microbes 2021;13:1-20. [PMID: 33522391 DOI: 10.1080/19490976.2021.1872323] [Cited by in Crossref: 26] [Cited by in F6Publishing: 17] [Article Influence: 26.0] [Reference Citation Analysis]
56 Whon TW, Shin N, Kim JY, Roh SW. Omics in gut microbiome analysis. J Microbiol 2021;59:292-7. [DOI: 10.1007/s12275-021-1004-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 8] [Article Influence: 1.0] [Reference Citation Analysis]
57 Jain T, Sharma P, Are AC, Vickers SM, Dudeja V. New Insights Into the Cancer-Microbiome-Immune Axis: Decrypting a Decade of Discoveries. Front Immunol 2021;12:622064. [PMID: 33708214 DOI: 10.3389/fimmu.2021.622064] [Cited by in Crossref: 7] [Cited by in F6Publishing: 29] [Article Influence: 7.0] [Reference Citation Analysis]
58 Sang C, Yan H, Chan WK, Zhu X, Sun T, Chang X, Xia M, Sun X, Hu X, Gao X, Jia W, Bian H, Chen T, Xie G. Diagnosis of Fibrosis Using Blood Markers and Logistic Regression in Southeast Asian Patients With Non-alcoholic Fatty Liver Disease. Front Med (Lausanne) 2021;8:637652. [PMID: 33708783 DOI: 10.3389/fmed.2021.637652] [Cited by in Crossref: 1] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
59 Gangadoo S, Rajapaksha Pathirannahalage P, Cheeseman S, Dang YTH, Elbourne A, Cozzolino D, Latham K, Truong VK, Chapman J. The Multiomics Analyses of Fecal Matrix and Its Significance to Coeliac Disease Gut Profiling. Int J Mol Sci 2021;22:1965. [PMID: 33671197 DOI: 10.3390/ijms22041965] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
60 Raza S, Kim J, Sadowsky MJ, Unno T. Microbial source tracking using metagenomics and other new technologies. J Microbiol 2021;59:259-69. [DOI: 10.1007/s12275-021-0668-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
61 Ruiz-Saavedra S, García-González H, Arboleya S, Salazar N, Emilio Labra-Gayo J, Díaz I, Gueimonde M, González S, de Los Reyes-Gavilán CG. Intestinal microbiota alterations by dietary exposure to chemicals from food cooking and processing. Application of data science for risk prediction. Comput Struct Biotechnol J 2021;19:1081-91. [PMID: 33680352 DOI: 10.1016/j.csbj.2021.01.037] [Reference Citation Analysis]
62 Koteluk O, Wartecki A, Mazurek S, Kołodziejczak I, Mackiewicz A. How Do Machines Learn? Artificial Intelligence as a New Era in Medicine. J Pers Med 2021;11:32. [PMID: 33430240 DOI: 10.3390/jpm11010032] [Cited by in Crossref: 6] [Cited by in F6Publishing: 16] [Article Influence: 6.0] [Reference Citation Analysis]
63 Ha CWY, Devkota S. The new microbiology: cultivating the future of microbiome-directed medicine. Am J Physiol Gastrointest Liver Physiol 2020;319:G639-45. [PMID: 32996782 DOI: 10.1152/ajpgi.00093.2020] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
64 Su X, Jing G, Zhang Y, Wu S. Method development for cross-study microbiome data mining: Challenges and opportunities. Comput Struct Biotechnol J 2020;18:2075-80. [PMID: 32802279 DOI: 10.1016/j.csbj.2020.07.020] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]