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Anna Bergamaschi

Researcher at University of Illinois at Urbana–Champaign

Publications -  34
Citations -  2941

Anna Bergamaschi is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 21, co-authored 27 publications receiving 2725 citations. Previous affiliations of Anna Bergamaschi include Rikshospitalet–Radiumhospitalet & Oslo University Hospital.

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Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer

TL;DR: A genome‐wide array‐based comparative genomic hybridization (array CGH) survey of CNAs in 89 breast tumors from a patient cohort with locally advanced disease links distinct cytoband loci harboring CNAs to specific clinicopathological parameters, including tumor grade, estrogen receptor status, presence of TP53 mutation, and overall survival.
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Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome

TL;DR: It is suggested that primary breast tumours can be classified on the basis of the expression of extracellular matrix (ECM) composition and that this classification provides relevant information on the biology of breast carcinomas, further supporting the hypothesis that clinical outcome is strongly related to stromal characteristics.
Posted Content

Regularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer

TL;DR: The proposed method remMap - REgularized Multivariate regression for identifying MAster Predictors - for fitting multivariate response regression models under the high-dimension-low-sample-size setting is applied to a breast cancer study, in which genome wide RNA transcript levels and DNA copy numbers were measured for 172 tumor samples.
Journal ArticleDOI

Regularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer

TL;DR: In this article, the authors proposed a new method remMap -regularized multivariate regression for identifying MAster Predictors -for fitting multivariate response regression models under the high-dimension-low-sample-size setting.