Machine learning algorithms are becoming increasingly popular for data crunching across a vast area of scientific disciplines. Here, the authors compare two machine learning algorithms with respect to accuracy and user-friendliness and find that random forest algorithms outperform logistic regression when applied to the same dataset.

In this study, the authors looked at a proto-oncogene, KRAS, and searched for molecules that are predicted to be able to bind to the inactive form of KRAS. They found that a modified version of Irbesartan, a derivative of benzimidazole, showed the best binding to inactive KRAS.

Superabsorbent beads are remarkable, used throughout our daily lives for various practical applications. These beads, as suggested by their name, possess a unique ability to absorb and retain large quantities of liquids. This characteristic of absorbency makes them essential throughout the medical field, agriculture, and other critical industries as well as in everyday products. To create these beads, the process of photopolymerization is fast growing in favor with distinct advantages of cost efficiency, speed, energy efficiency, and mindfulness towards the environment. In this article, researchers explore the pairing of cheap monomers with accessible equipment for creation of superabsorbent beads via the photopolymerization process. This research substantially demonstrates the successful application of photopolymerization in producing highly absorbent beads in a low-cost context, thereby expanding the accessibility of this process for creating superabsorbent beads in both research and practical applications.

The authors investigate the ability of machine learning models to developing new drug-like molecules by learning desired chemical properties versus simply generating molecules that similar to those in the training set.

In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.

A trial study was performed in 2021 to investigate the link between technology and transactive memory. Transactive memory is shared knowledge in which members share the responsibility to encode, store, and retrieve certain tasks or assignments, leading to a successful and collective performance. We hypothesize that a participants’ expected access to an external source affects the recall rate and retrieval of information.

Thymoquinone is a compound of great therapeutic potential and scientific interest. However, its clinical administration and synthetic modifications are greatly limited by its instability in the presence of light. This study employed quantitative 1H nuclear magnetic resonance (NMR) spectroscopy to identify the effect of solvation on the degradation of thymoquinone under ultraviolet light (UV). It found that the rate of degradation is highly solvent dependent occurs maximally in chloroform.

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.