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Full-Text Articles in Electrical and Computer Engineering
Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi
Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi
Masters Theses
Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.
Bioprinted Mcf7 Breast Cancer Cells, An In Vitro Model For Drug Discovery, Aleli Campbell
Bioprinted Mcf7 Breast Cancer Cells, An In Vitro Model For Drug Discovery, Aleli Campbell
Open Access Theses & Dissertations
Current breast cancer treatments are successful in eradicating this disease in the majority of patients, though there are quite a few cases where relapse or recurrence follow, which may lead to continued cancer therapy or death. Thermal inkjet bioprinting (BP) is a novel technique that is used to bioprint biomaterials or diverse cellular organisms to engineer tissue or organ models in vitro. In this Dissertation, we investigated the molecular effect of BP MCF7 breast cancer cells (BCC), cell survival of the cells when exposed to FDA approved systemic therapy alone and in combination with radiation and lastly, the ability of …