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For: Pan Y, Lei X, Zhang Y. Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach. Med Res Rev 2021. [PMID: 34346083 DOI: 10.1002/med.21847] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Yang G, Zhou S, He H, Shen Z, Liu Y, Hu J, Wang J. Exploring the “gene–protein–metabolite” network of coronary heart disease with phlegm and blood stasis syndrome by integrated multi-omics strategy. Front Pharmacol 2022;13. [DOI: 10.3389/fphar.2022.1022627] [Reference Citation Analysis]
2 Guo Y, Lei X. A pseudo-Siamese framework for circRNA-RBP binding sites prediction integrating BiLSTM and soft attention mechanism. Methods 2022;207:57-64. [PMID: 36113743 DOI: 10.1016/j.ymeth.2022.09.003] [Reference Citation Analysis]
3 Shen Z, Hu J, Wu H, Chen Z, Wu W, Lin J, Xu Z, Kong J, Lin T. Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study. J Transl Med 2022;20:409. [PMID: 36068536 DOI: 10.1186/s12967-022-03615-0] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
4 Presti D, Dall'Olio FG, Besse B, Ribeiro JM, Di Meglio A, Soldato D. Tumor infiltrating lymphocytes (TILs) as a predictive biomarker of response to checkpoint blockers in solid tumors: a systematic review. Crit Rev Oncol Hematol 2022;:103773. [PMID: 35917885 DOI: 10.1016/j.critrevonc.2022.103773] [Reference Citation Analysis]
5 Hajjo R, Sabbah DA, Al Bawab AQ. Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers. Diagnostics 2022;12:1742. [DOI: 10.3390/diagnostics12071742] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022;10:e004848. [PMID: 35793875 DOI: 10.1136/jitc-2022-004848] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Stella GM, Scialò F, Bortolotto C, Agustoni F, Sanci V, Saddi J, Casali L, Corsico AG, Bianco A. Pragmatic Expectancy on Microbiota and Non-Small Cell Lung Cancer: A Narrative Review. Cancers 2022;14:3131. [DOI: 10.3390/cancers14133131] [Reference Citation Analysis]
8 Chen Y, Lei X. Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction. Front Microbiol 2022;13:919380. [PMID: 35711758 DOI: 10.3389/fmicb.2022.919380] [Reference Citation Analysis]
9 Wei S, Wei Y, Gong Y, Chen Y, Cui J, Li L, Yan H, Yu Y, Lin X, Li G, Yi L. Metabolomics as a valid analytical technique in environmental exposure research: application and progress. Metabolomics 2022;18:35. [PMID: 35639180 DOI: 10.1007/s11306-022-01895-7] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Wang Y, Lei X, Pan Y. Predicting Microbe‐Disease Association Based on Heterogeneous Network and Global Graph Feature Learning. Chinese J of Electronics 2022;31:345-53. [DOI: 10.1049/cje.2020.00.212] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
11 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022;12:773840. [DOI: 10.3389/fonc.2022.773840] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
12 Kou Z, Huang YF, Shen A, Kosari S, Liu XR, Qiang XL. Prediction of pandemic risk for animal-origin coronavirus using a deep learning method. Infect Dis Poverty 2021;10:128. [PMID: 34689829 DOI: 10.1186/s40249-021-00912-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]