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Full-Text Articles in Chemicals and Drugs
Design, Synthesis, And Antiproliferative Activity Of Benzopyran-4-One-Isoxazole Hybrid Compounds, Shilpi Gupta, Shang Eun Park, Saghar Mozaffari, Bishoy El-Aarag, Keykavous Parang, Rakesh Kumar Tiwari
Design, Synthesis, And Antiproliferative Activity Of Benzopyran-4-One-Isoxazole Hybrid Compounds, Shilpi Gupta, Shang Eun Park, Saghar Mozaffari, Bishoy El-Aarag, Keykavous Parang, Rakesh Kumar Tiwari
Pharmacy Faculty Articles and Research
The biological significance of benzopyran-4-ones as cytotoxic agents against multi-drug resistant cancer cell lines and isoxazoles as anti-inflammatory agents in cellular assays prompted us to design and synthesize their hybrid compounds and explore their antiproliferative activity against a panel of six cancer cell lines and two normal cell lines. Compounds 5a–d displayed significant antiproliferative activities against all the cancer cell lines tested, and IC50 values were in the range of 5.2–22.2 μM against MDA-MB-231 cancer cells, while they were minimally cytotoxic to the HEK-293 and LLC-PK1 normal cell lines. The IC50 values of 5a–d …
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 …