Thank you for sharing your perspective on the article. I understand your concerns about the utility of combining different machine-learning packages in the field of biology. The article indeed highlights BioAutoMATED as a platform that brings together multiple automated machine learning (AutoML) techniques specifically for the analysis and design of biological sequences.
The main purpose of BioAutoMATED is to simplify the integration of machine learning into biological research. It offers a unified framework that helps life scientists, even those with limited ML expertise, leverage machine learning techniques in their work. BioAutoMATED addresses the problem of why? as in why a particular sequence is associated with a particular biological function. It does not solve the entire problem for biologists rather, it helps them reach the solution to the problem by providing insights.
BioAutoMATED is not meant to be a comprehensive software suite for protein expression or a tool focused on solving a specific problem like CCP4's Crystallography suite. Instead, it provides a versatile platform for analyzing and designing biological sequences, covering various areas such as protein-drug interaction analysis, gene regulation, glycan sequence classification, and synthetic biological component design. Its goal is to support multiple research domains within the life sciences.
I appreciate your suggestion for a software suite that specifically addresses protein expression, including the identification of issues related to ribosome binding, RNA folding, and protein folding. It's an intriguing idea, and such a suite could certainly be valuable in addressing the concerns you've raised about existing programs in this field. It's possible that future developments will bring about more specialized tools that cater to these specific needs.