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Full-Text Articles in Diseases
Application Of Rnai-Induced Gene Expression Profiles For Prognostic Prediction In Breast Cancer, Yue Wang, Kenneth . M. K. Mark, Matthew H. Ung, Arminja Kettenbach, Todd Miller, Wei Xu, Wenqing Cheng Cheng, Tian Xia, Chao Cheng
Application Of Rnai-Induced Gene Expression Profiles For Prognostic Prediction In Breast Cancer, Yue Wang, Kenneth . M. K. Mark, Matthew H. Ung, Arminja Kettenbach, Todd Miller, Wei Xu, Wenqing Cheng Cheng, Tian Xia, Chao Cheng
Dartmouth Scholarship
Homologous recombination (HR) is the primary pathway for repairing double-strand DNA breaks implicating in the development of cancer. RNAi-based knockdowns of BRCA1 and RAD51 in this pathway have been performed to investigate the resulting transcriptomic profiles. Here we propose a computational framework to utilize these profiles to calculate a score, named RNA-Interference derived Proliferation Score (RIPS), which reflects cell proliferation ability in individual breast tumors. RIPS is predictive of breast cancer classes, prognosis, genome instability, and neoadjuvant chemosensitivity. This framework directly translates the readout of knockdown experiments into potential clinical applications and generates a robust biomarker in breast cancer.
Detecting Gene-Gene Interactions Using A Permutation-Based Random Forest Method, Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore
Detecting Gene-Gene Interactions Using A Permutation-Based Random Forest Method, Jing Li, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore
Dartmouth Scholarship
Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions.