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For: Yu KH, Berry GJ, Rubin DL, Ré C, Altman RB, Snyder M. Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma. Cell Syst 2017;5:620-627.e3. [PMID: 29153840 DOI: 10.1016/j.cels.2017.10.014] [Cited by in Crossref: 36] [Cited by in F6Publishing: 40] [Article Influence: 7.2] [Reference Citation Analysis]
Number Citing Articles
1 Yu K, Kohane IS. Framing the challenges of artificial intelligence in medicine. BMJ Qual Saf 2019;28:238-41. [DOI: 10.1136/bmjqs-2018-008551] [Cited by in Crossref: 71] [Cited by in F6Publishing: 48] [Article Influence: 17.8] [Reference Citation Analysis]
2 Chen L, Zeng H, Xiang Y, Huang Y, Luo Y, Ma X. Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma. Front Cell Dev Biol 2021;9:720110. [PMID: 34708036 DOI: 10.3389/fcell.2021.720110] [Reference Citation Analysis]
3 Fu Y, Jung AW, Torne RV, Gonzalez S, Vöhringer H, Shmatko A, Yates LR, Jimenez-linan M, Moore L, Gerstung M. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 2020;1:800-10. [DOI: 10.1038/s43018-020-0085-8] [Cited by in Crossref: 55] [Cited by in F6Publishing: 19] [Article Influence: 27.5] [Reference Citation Analysis]
4 Zhang L, Li Y, Dai Y, Wang D, Wang X, Cao Y, Liu W, Tao Z. Glycolysis-related gene expression profiling serves as a novel prognosis risk predictor for human hepatocellular carcinoma. Sci Rep 2021;11:18875. [PMID: 34556750 DOI: 10.1038/s41598-021-98381-2] [Reference Citation Analysis]
5 Chakraborty N, Schmitt CW, Honnold CL, Moyler C, Butler S, Nachabe H, Gautam A, Hammamieh R. Protocol Improvement for RNA Extraction From Compromised Frozen Specimens Generated in Austere Conditions: A Path Forward to Transcriptomics-Pathology Systems Integration. Front Mol Biosci 2020;7:142. [PMID: 32793629 DOI: 10.3389/fmolb.2020.00142] [Reference Citation Analysis]
6 Zhang S, Fan Y, Zhong T, Ma S. Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling. Sci Rep 2020;10:15030. [PMID: 32929170 DOI: 10.1038/s41598-020-72201-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
7 Li H, Chen L, Zeng H, Liao Q, Ji J, Ma X. Integrative Analysis of Histopathological Images and Genomic Data in Colon Adenocarcinoma. Front Oncol 2021;11:636451. [PMID: 34646756 DOI: 10.3389/fonc.2021.636451] [Reference Citation Analysis]
8 Zhong T, Wu M, Ma S. Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer. Cancers (Basel) 2019;11:E361. [PMID: 30871256 DOI: 10.3390/cancers11030361] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 3.7] [Reference Citation Analysis]
9 Patel SK, George B, Rai V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Front Pharmacol 2020;11:1177. [PMID: 32903628 DOI: 10.3389/fphar.2020.01177] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
10 Wulczyn E, Steiner DF, Xu Z, Sadhwani A, Wang H, Flament-Auvigne I, Mermel CH, Chen PC, Liu Y, Stumpe MC. Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS One. 2020;15:e0233678. [PMID: 32555646 DOI: 10.1371/journal.pone.0233678] [Cited by in Crossref: 31] [Cited by in F6Publishing: 25] [Article Influence: 15.5] [Reference Citation Analysis]
11 Ektefaie Y, Yuan W, Dillon DA, Lin NU, Golden JA, Kohane IS, Yu KH. Integrative multiomics-histopathology analysis for breast cancer classification. NPJ Breast Cancer 2021;7:147. [PMID: 34845230 DOI: 10.1038/s41523-021-00357-y] [Reference Citation Analysis]
12 Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
13 Yu KH, Lee TM, Chen YJ, Ré C, Kou SC, Chiang JH, Snyder M, Kohane IS. A Cloud-Based Metabolite and Chemical Prioritization System for the Biology/Disease-Driven Human Proteome Project. J Proteome Res 2018;17:4345-57. [PMID: 30094994 DOI: 10.1021/acs.jproteome.8b00378] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
14 Alkaitis MS, Agrawal MN, Riely GJ, Razavi P, Sontag D. Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer. JCO Clin Cancer Inform 2021;5:550-60. [PMID: 33989016 DOI: 10.1200/CCI.20.00139] [Reference Citation Analysis]
15 [DOI: 10.1101/813543] [Cited by in Crossref: 14] [Cited by in F6Publishing: 7] [Reference Citation Analysis]
16 Azuaje F, Kim SY, Perez Hernandez D, Dittmar G. Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning. J Clin Med 2019;8:E1535. [PMID: 31557788 DOI: 10.3390/jcm8101535] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
17 Karobari FM, Suresh HN. Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique. Journal of Intelligent Systems 2019;29:1301-14. [DOI: 10.1515/jisys-2018-0316] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
18 Yu KH, Wang F, Berry GJ, Ré C, Altman RB, Snyder M, Kohane IS. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc 2020;27:757-69. [PMID: 32364237 DOI: 10.1093/jamia/ocz230] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
19 Ren M, Zhang Q, Zhang S, Zhong T, Huang J, Ma S. Hierarchical cancer heterogeneity analysis based on histopathological imaging features. Biometrics 2021. [PMID: 34390584 DOI: 10.1111/biom.13544] [Reference Citation Analysis]
20 Song Y, Chen D, Zhang X, Luo Y, Li S. Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma. Thorac Cancer 2019;10:1220-8. [PMID: 30993904 DOI: 10.1111/1759-7714.13072] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
21 Noorbakhsh J, Farahmand S, Foroughi Pour A, Namburi S, Caruana D, Rimm D, Soltanieh-Ha M, Zarringhalam K, Chuang JH. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun 2020;11:6367. [PMID: 33311458 DOI: 10.1038/s41467-020-20030-5] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
22 Yu K, Lee TM, Wang C, Chen Y, Ré C, Kou SC, Chiang J, Kohane IS, Snyder M. Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining. J Proteome Res 2018;17:1383-96. [DOI: 10.1021/acs.jproteome.7b00772] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 4.0] [Reference Citation Analysis]
23 Xu Y, Zhong T, Wu M, Ma S. Histopathological Imaging⁻Environment Interactions in Cancer Modeling. Cancers (Basel) 2019;11:E579. [PMID: 31022926 DOI: 10.3390/cancers11040579] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.7] [Reference Citation Analysis]
24 Yu KH, Lee TM, Yen MH, Kou SC, Rosen B, Chiang JH, Kohane IS. Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation. J Med Internet Res 2020;22:e16709. [PMID: 32755895 DOI: 10.2196/16709] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
25 Karimi MR, Karimi AH, Abolmaali S, Sadeghi M, Schmitz U. Prospects and challenges of cancer systems medicine: from genes to disease networks. Brief Bioinform 2021:bbab343. [PMID: 34471925 DOI: 10.1093/bib/bbab343] [Reference Citation Analysis]
26 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World J Gastroenterol 2021; 27(44): 7687-7704 [PMID: 34908807 DOI: 10.3748/wjg.v27.i44.7687] [Reference Citation Analysis]
27 Azuaje F. Artificial intelligence for precision oncology: beyond patient stratification. NPJ Precis Oncol 2019;3:6. [PMID: 30820462 DOI: 10.1038/s41698-019-0078-1] [Cited by in Crossref: 35] [Cited by in F6Publishing: 32] [Article Influence: 11.7] [Reference Citation Analysis]
28 Noh KW, Buettner R, Klein S. Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis. Biomolecules 2021;11:1310. [PMID: 34572523 DOI: 10.3390/biom11091310] [Reference Citation Analysis]
29 Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2:719-731. [PMID: 31015651 DOI: 10.1038/s41551-018-0305-z] [Cited by in Crossref: 380] [Cited by in F6Publishing: 256] [Article Influence: 95.0] [Reference Citation Analysis]
30 Chen L, Zeng H, Zhang M, Luo Y, Ma X. Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma. Cancer Med 2021;10:4615-28. [PMID: 33987946 DOI: 10.1002/cam4.3965] [Reference Citation Analysis]
31 [DOI: 10.1101/530360] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
32 Si H, Du D, Li W, Li Q, Li J, Zhao D, Li L, Tang B. Sputum-Based Tumor Fluid Biopsy: Isolation and High-Throughput Single-Cell Analysis of Exfoliated Tumor Cells for Lung Cancer Diagnosis. Anal Chem 2021;93:10477-86. [PMID: 34292723 DOI: 10.1021/acs.analchem.1c00833] [Reference Citation Analysis]
33 Panja S, Hayati S, Epsi NJ, Parrott JS, Mitrofanova A. Integrative (epi) Genomic Analysis to Predict Response to Androgen-Deprivation Therapy in Prostate Cancer. EBioMedicine 2018;31:110-21. [PMID: 29685789 DOI: 10.1016/j.ebiom.2018.04.007] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
34 Jain MS, Massoud TF. Predicting tumour mutational burden from histopathological images using multiscale deep learning. Nat Mach Intell 2020;2:356-62. [DOI: 10.1038/s42256-020-0190-5] [Cited by in Crossref: 8] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
35 Xia L, Yang F, Wu X, Li S, Kan C, Zheng H, Wang S. SHP2 inhibition enhances the anticancer effect of Osimertinib in EGFR T790M mutant lung adenocarcinoma by blocking CXCL8 loop mediated stemness. Cancer Cell Int 2021;21:337. [PMID: 34217295 DOI: 10.1186/s12935-021-02056-x] [Reference Citation Analysis]
36 Kimmel JC, Yi N, Roy M, Hendrickson DG, Kelley DR. Differentiation reveals latent features of aging and an energy barrier in murine myogenesis. Cell Rep 2021;35:109046. [PMID: 33910007 DOI: 10.1016/j.celrep.2021.109046] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
37 Yu KH, Hu V, Wang F, Matulonis UA, Mutter GL, Golden JA, Kohane IS. Deciphering serous ovarian carcinoma histopathology and platinum response by convolutional neural networks. BMC Med 2020;18:236. [PMID: 32807164 DOI: 10.1186/s12916-020-01684-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
38 Zeng H, Chen L, Huang Y, Luo Y, Ma X. Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma. Front Cell Dev Biol 2020;8:553099. [PMID: 33195188 DOI: 10.3389/fcell.2020.553099] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
39 Cho HJ, Lee S, Ji YG, Lee DH. Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma. PLoS One 2018;13:e0207204. [PMID: 30419062 DOI: 10.1371/journal.pone.0207204] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 3.3] [Reference Citation Analysis]
40 Zhao K, Li Z, Li Y, Yao S, Huang Y, Wang Y, Zhang F, Wu L, Chen X, Liang C, Liu Z. Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis. Oncoimmunology 2020;9:1841935. [PMID: 33194320 DOI: 10.1080/2162402X.2020.1841935] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
41 Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z, Wu L, Huang Y, Liang C, Liu Z. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine. 2020;61:103054. [PMID: 33039706 DOI: 10.1016/j.ebiom.2020.103054] [Cited by in Crossref: 11] [Cited by in F6Publishing: 16] [Article Influence: 5.5] [Reference Citation Analysis]
42 Liu K, Li K, Wu T, Liang M, Zhong Y, Yu X, Li X, Xie C, Zhang L, Liu X. Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT. Eur Radiol 2021. [PMID: 34453574 DOI: 10.1007/s00330-021-08194-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
43 Cortés-Ciriano I, Bender A. Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout. J Chem Inf Model 2019;59:3330-9. [PMID: 31241929 DOI: 10.1021/acs.jcim.9b00297] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]