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Cited by in F6Publishing
For: Tavolara TE, Niazi MKK, Gower AC, Ginese M, Beamer G, Gurcan MN. Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice. EBioMedicine 2021;67:103388. [PMID: 34000621 DOI: 10.1016/j.ebiom.2021.103388] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Arlova A, Jin C, Wong-Rolle A, Chen ES, Lisle C, Brown GT, Lay N, Choyke PL, Turkbey B, Harmon S, Zhao C. Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma. J Pathol Inform 2022;13:100007. [PMID: 35242446 DOI: 10.1016/j.jpi.2022.100007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Koyuncu D, Niazi MKK, Tavolara T, Abeijon C, Ginese ML, Liao Y, Mark C, Specht A, Gower AC, Restrepo BI, Gatti DM, Kramnik I, Gurcan M, Yener B, Beamer G. CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice. PLoS Pathog 2021;17:e1009773. [PMID: 34403447 DOI: 10.1371/journal.ppat.1009773] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Phan NN, Huang C, Tseng L, Chuang EY. Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images. Front Oncol 2021;11:769447. [DOI: 10.3389/fonc.2021.769447] [Reference Citation Analysis]