Applying machine learning to genome-wide association and electronic health record data may usher in a new era of precision in ...

Herein, a fluorescence-based dynamic regulation (FIRM) and intrinsic-feature clustering method was proposed to regulate nucleic acid amplification in microporous chips and improve the analytical ...

To avoid these difficulties, classification algorithms can be used to detect the presence and location of brain lesions or identify the lesion type. During the last decades, several research ...

The ML Algorithm Selector is an interactive desktop application built with Python and Tkinter. It guides users through a decision-making process to identify suitable machine learning algorithms for ...

Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset ...

Certain preprocessing techniques were used to improve accuracy and outcomes. Ultimately, we employed decision trees, logistic regression, and random forests to reach our objective. Of these, random ...

While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine ...

Explainability analysis is a very relevant topic today, due to the interest of allowing the interpretability of machine learning models. In this work, we carry out an in-depth study of explainability ...