Integrative Functional Analysis Improves Information Retrieval in Breast Cancer

2015 ◽  
pp. 43-50 ◽  
Author(s):  
Juan Cruz Rodriguez ◽  
Germán González ◽  
Cristobal Fresno ◽  
Elmer A. Fernández
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Information Retrieval

scholarly journals Erratum: Functional analysis of MKP-1 and MKP-2 in breast cancer tamoxifen sensitivity

Oncotarget ◽  
2018 ◽  
Vol 9 (81) ◽  
pp. 35286-35286
Author(s):  
Kelly K. Haagenson ◽  
Jessica Wei Zhang ◽  
Zhengfan Xu ◽  
Malathy P.V. Shekhar ◽  
Gen Sheng Wu
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Tamoxifen Sensitivity

Abstract 4700: Identification and functional analysis of 9p23-24 amplified genes in human breast cancer

2011 ◽  
Author(s):  
Jack Wu ◽  
Suling Liu ◽  
Gang Liu ◽  
Alan Dombkowski ◽  
Judith Abrams ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Human Breast Cancer ◽  
Human Breast

scholarly journals Characteristic Gene Expression Profiles of Human Fibroblasts and Breast Cancer Cells in a Newly Developed Bilateral Coculture System

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Takayuki Ueno ◽  
Jun Utsumi ◽  
Masakazu Toi ◽  
Kazuharu Shimizu
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Breast Cancer Cells ◽  
Human Fibroblasts ◽  
Breast Cancer Cell Lines ◽  
Collagen Membrane ◽  
Coculture System ◽  
Stromal Component

The microenvironment of cancer cells has been implicated in cancer development and progression. Cancer-associated fibroblast constitutes a major stromal component of the microenvironment. To analyze interaction between cancer cells and fibroblasts, we have developed a new bilateral coculture system using a two-sided microporous collagen membrane. Human normal skin fibroblasts were cocultured with three different human breast cancer cell lines: MCF-7, SK-BR-3, and HCC1937. After coculture, mRNA was extracted separately from cancer cells and fibroblasts and applied to transcriptomic analysis with microarray. Top 500 commonly up- or downregulated genes were characterized by enrichment functional analysis using MetaCore Functional Analysis. Most of the genes upregulated in cancer cells were downregulated in fibroblasts while most of the genes downregulated in cancer cells were upregulated in fibroblasts, indicating that changing patterns of mRNA expression were reciprocal between cancer cells and fibroblasts. In coculture, breast cancer cells commonly increased genes related to mitotic response and TCA pathway while fibroblasts increased genes related to carbohydrate metabolism including glycolysis, glycogenesis, and glucose transport, indicating that fibroblasts support cancer cell proliferation by supplying energy sources. We propose that the bilateral coculture system using collagen membrane is useful to study interactions between cancer cells and stromal cells by mimicking in vivo tumor microenvironment.

scholarly journals Epigenetic mapping and functional analysis in a breast cancer metastasis model using whole-genome promoter tiling microarrays

2008 ◽  
Vol 10 (4) ◽  
Author(s):  
David I Rodenhiser ◽  
Joseph Andrews ◽  
Wendy Kennette ◽  
Bekim Sadikovic ◽  
Ariel Mendlowitz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Whole Genome ◽  
Tiling Microarrays ◽  
Metastasis Model

Functional Analysis of the Breast Cancer Genome

2006 ◽  
Vol 24 (11) ◽  
pp. 1649-1650 ◽  
Author(s):  
Matthew J. Ellis
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Cancer Genome

Expression and functional analysis of Bax inhibitor-1 in human breast cancer cells

2006 ◽  
Vol 208 (3) ◽  
pp. 340-349 ◽  
Author(s):  
Michal Grzmil ◽  
Silke Kaulfuss ◽  
Paul Thelen ◽  
Bernhard Hemmerlein ◽  
Stefan Schweyer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Cancer Cells ◽  
Human Breast Cancer ◽  
Breast Cancer Cells ◽  
Human Breast Cancer Cells ◽  
Human Breast

scholarly journals Intelligent information retrieval for reducing missed cancer and improving the healthcare system

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Breast Cancer ◽  
Information Retrieval ◽  
Language Processing ◽  
Missing Values ◽  
Classification Model ◽  
Filter Method ◽  
Breast Cancer Dataset ◽  
Free Text ◽  
Intelligent Information Retrieval ◽  
Intelligent Information

This study presents an intelligent information retrieval system that will effectively extract useful information from breast cancer datasets and utilized that information to build a classification model. The proposed model will reduce the missed cancer rate by providing a comprehensive decision support to the radiologist. The model is built on two datasets, Wisconsin Breast Cancer Dataset (WBCD) and 365 free text mammography reports from a hospital. Effective pre-processing techniques including filling missing values with regression, an effective Natural Language Processing (NLP) Parser is developed to handle free text mammography reports, balancing the dataset with Synthetic Minority Oversampling (SMOTE) was applied to prepare the dataset for learning. Most relevant features were selected with the help of filter method and tf-idf scores. K-NN and SGD classifiers are optimized with optimum value of k for K-NN and hyper tuning the SGD parameters with grid search technique.

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