iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis,...

Area of Science:

• Bioinformatics
• Machine Learning
• Computational Biology

Background:

• Sequence-based analysis is crucial for understanding DNA, RNA, and protein function.
• The rapid growth of sequence data necessitates advanced predictive modeling tools.
• Existing tools often require programming expertise, limiting accessibility.

Purpose of the Study:

• Introduce iLearnPlus, the first graphical and web-based machine learning platform for sequence analysis.
• Automate the construction and deployment of machine learning pipelines for sequence data.
• Provide a comprehensive, user-friendly solution for both bioinformaticians and biologists.

Main Methods:

• Developed a platform with graphical and web-based interfaces for machine learning pipeline construction.
• Integrated automated feature extraction, model building, performance assessment, and data visualization.
• Included a wide range of feature sets and over twenty machine learning algorithms, including deep learning approaches.

Main Results:

• iLearnPlus successfully automates sequence-based feature extraction, model construction, and prediction without requiring programming.
• The platform offers a broad selection of feature sets and machine learning algorithms, exceeding current solutions.
• Demonstrated utility through case studies predicting long noncoding RNAs and protein crotonylation sites.

Conclusions:

• iLearnPlus empowers researchers, from experienced bioinformaticians to biologists without coding skills, to perform advanced sequence analysis and prediction.
• The platform's comprehensive features and intuitive design accelerate discovery in molecular biology.
• iLearnPlus is an open-source tool, promoting accessibility and further development in the field.