A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer.

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models.

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Preparation and Characterization of Blank and Nerolidol-Loaded Chitosan-Alginate Nanoparticles.

Recently, there has been a growing interest in using natural products as treatment alternatives in several diseases. Nerolidol is a natural product which has been shown to have protective effects in several conditions. The low water solubility of nerolidol and many other natural products limits their delivery to the body. In this research, a drug delivery system composed of alginate and chitosan was fabricated and loaded with nerolidol to enhance its water solubility. The chitosan-alginate nanoparticles were fabricated using a new method including the tween 80 pre-gelation, followed by poly-ionic crosslinking between chitosan negative and alginate positive groups. Several characterization techniques were used to validate the fabricated nanoparticles. The molecular interactions between the chitosan, alginate, and nerolidol molecules were confirmed using the Fourier transform infrared spectroscopy. The ultraviolet spectroscopy showed an absorbance peak of the blank nanoparticles at 200 nm and for the pure nerolidol at 280 nm. Using both scanning and transmission electron microscopy, the nanoparticles were found to be spherical in shape with an average size of 12 nm and 35 nm for the blank chitosan-alginate nanoparticles and the nerolidol-loaded chitosan-alginate nanoparticles, respectively. The nanoparticles were also shown to have a loading capacity of 51.7% and an encapsulation efficiency of 87%. A controlled release profile of the loaded drug for up to 28 h using an in vitro model was also observed, which is more efficient than the free form of nerolidol. In conclusion, chitosan-alginate nanoparticles and nerolidol loaded chitosan-alginate nanoparticles were successfully fabricated and characterized to show potential encapsulation and delivery using an in vitro model.