Gene expression profiling predicts clinical outcome of breast cancer

By L. J. Veer, H. Dai, M. V. Vijver, Yudong He, A. Hart, M. Mao, H. Peterse, K. V. D. Kooy, M. Marton, A. Witteveen and 6 more. 2002

9,976 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology, Computational Biology & Bioinformatics, Medical & Health AI

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, background): “In the past decade, extensive work has been done to identify differentially expressed genes across different phenotypes, which can be used as diagnostic markers for classifying different disease states or predicting clinical outcomes [4-7].”
Gene selection and classification of microarray data using random forest (2006, neutral): “Breast [9] 4869 78 2 Breast [9] 4869 96 3”
Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction (2008, neutral): “16 of which are presented in our data, from [18] and the overview section of breast cancer (MIM 114480) in Online Mendelian Inheritance in Man (May, 2007)3.”
IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures (2020, background): “Gene signatures for chemotherapy response prediction have also been reported: the 70-gene (3) and 21-gene (4) assays predict distant recurrence of estrogen receptor positive breast cancer with adjuvant chemotherapy, with the aforementioned TMEscore further promising as a biomarker for chemotherapy s”
REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms (2011, background): “…# 1, a comparative analysis of predicted gene expression levels in bacteria and archaea [15]; example # 2, finding putative targets for the cytostatic activity of a small molecule against cancer cell lines [16]; and example # 3, gene expression profiling of aggressive breast cancer samples [17].”
A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data (2012, neutral): “[27] is different from the Dutch 70 scoring system [25, 26].”
A Bayesian missing value estimation method for gene expression profile data (2003, background): “Gene expression profile analyses were also effective in cancer prognosis prediction, even when morphological or immunohistological study was difficult (Alizadeh et al., 2000; Kihara et al., 2001; Pomeroy et al., 2002; Shipp et al., 2002; van’t Veer et al., 2002).”
Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach (2015, background): “For instance, [5] has shown that breast cancer patients with a good-prognosis genetic signature can have longer survival time than those with a poorprognosis genetic profile.”