The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new ...

Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...

Cancer Research has advanced during the past few years. Using high throughput technology and advances in artificial intelligence, it is now possible to improve cancer diagnosis and targeted therapy, ...

While numerous highly accurate models, specifically for property prediction, have been reported in the literature, there has been a lack of a generalized framework. Herein we propose a novel feature ...

Abstract MicroRNAs (miRNAs) play a pivotal role in gene expression regulation and are closely linked to cancer development. In this study, we employ machine learning techniques to identify critical ...

Discover key strategies to enhance feature selection for more effective machine learning models, ensuring accuracy and efficiency.

This research introduces an innovative approach to sentiment analysis, addressing scalability challenges inherent in deep learning models. Chi-Vec, a fused feature selection technique, optimizes model ...

An analytical model based on machine learning algorithms with supervised learning, i.e., a k -NN classifier with known class membership, was proposed and used to evaluate the effects of the VR program ...