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Full-Text Articles in Nucleic Acids, Nucleotides, and Nucleosides
A Systematic Comparison Of Lipopolymers For Sirna Delivery To Multiple Breast Cancer Cell Lines: In Vitro Studies, Hamidreza Montazeri Aliabadi, Remant Bahadur Kc, Emira Bousoik, Ashley Barbarino, Bindu Thapa, Melissa Coyle, Parvin Mahdipoor, Hasan Uludağ
A Systematic Comparison Of Lipopolymers For Sirna Delivery To Multiple Breast Cancer Cell Lines: In Vitro Studies, Hamidreza Montazeri Aliabadi, Remant Bahadur Kc, Emira Bousoik, Ashley Barbarino, Bindu Thapa, Melissa Coyle, Parvin Mahdipoor, Hasan Uludağ
Pharmacy Faculty Articles and Research
Small interfering RNA (siRNA) therapy is a promising approach for treatment of a wide range of cancers, including breast cancers that display variable phenotypic features. To explore the general utility of siRNA therapy to control aberrant expression of genes in breast cancer, we conducted a detailed analysis of siRNA delivery and silencing response in vitro in 6 separate breast cancer cell models (MDA-MB-231, MDA-MB-231-KRas-CRM, MCF-7, AU565, MDA-MB-435 and MDA-MB-468 cells). Using lipopolymers for siRNA complexation and delivery, we found a large variation in siRNA delivery efficiency depending on the specific lipopolymer used for siRNA complexation and delivery. Some lipopolymers were …
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker
Mathematics, Physics, and Computer Science Faculty Articles and Research
Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …