scholarly journals Genetic polymorphisms in RNA binding proteins contribute to breast cancer survival

2012 ◽  
Vol 132 (3) ◽  
pp. E128-E138 ◽  
Author(s):  
Rohit Upadhyay ◽  
Sandhya Sanduja ◽  
Vimala Kaza ◽  
Dan A. Dixon
Keyword(s):  
Breast Cancer ◽  
Genetic Polymorphisms ◽  
Binding Proteins ◽  
Cancer Survival ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Breast Cancer Survival

Genetic Polymorphisms in the µ-Opioid Receptor Gene and Breast Cancer Survival

2012 ◽  
pp. 95-112
Author(s):  
Andrey V. Bortsov ◽  
Robert C. Millikan ◽  
Inna Belfer ◽  
Richard L. Boortz-Marx ◽  
Harendra Arora ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Polymorphisms ◽  
Opioid Receptor ◽  
Cancer Survival ◽  
Receptor Gene ◽  
Breast Cancer Survival

scholarly journals Common genetic polymorphisms of microRNA biogenesis pathway genes and breast cancer survival

BMC Cancer ◽  
2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Hyuna Sung ◽  
Sujee Jeon ◽  
Kyoung-Mu Lee ◽  
Sohee Han ◽  
Minkyo Song ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Polymorphisms ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Microrna Biogenesis

Genetic polymorphisms in estrogen-metabolizing genes and breast cancer survival

2007 ◽  
Vol 17 (5) ◽  
pp. 331-338 ◽  
Author(s):  
Ji-Rong Long ◽  
Qiuyin Cai ◽  
Xiao-Ou Shu ◽  
Hui Cai ◽  
Yu-Tang Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Polymorphisms ◽  
Cancer Survival ◽  
Breast Cancer Survival

scholarly journals Genetic Polymorphisms of Metastasis Suppressor Gene NME1 and Breast Cancer Survival

2008 ◽  
Vol 14 (15) ◽  
pp. 4787-4793 ◽  
Author(s):  
Shimian Qu ◽  
Jirong Long ◽  
Qiuyin Cai ◽  
Xiao-Ou Shu ◽  
Hui Cai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Polymorphisms ◽  
Cancer Survival ◽  
Suppressor Gene ◽  
Breast Cancer Survival ◽  
Metastasis Suppressor ◽  
Metastasis Suppressor Gene

scholarly journals Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

2019 ◽  
Author(s):  
Andrés López-Cortés ◽  
Alejandro Cabrera-Andrade ◽  
José M. Vázquez-Naya ◽  
Alejandro Pazos ◽  
Humberto Gonzáles-Díaz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Immunotherapy ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Operating Characteristics ◽  
Associated Proteins ◽  
Artificial Neural ◽  
New Biomarkers

ABSTRACTBackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design.MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features.ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037 and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1.ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

Abstract PD15-07: Myc regulates alternative splicing through a network of RNA binding proteins in breast cancer

2021 ◽  
Author(s):  
Laura Urbanski ◽  
Brittany Angarola ◽  
Mattia Brugiolo ◽  
Marina Yurieva ◽  
Sunghee Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Alternative Splicing ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins
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