Ten quick tips for sequence-based prediction of protein properties using machine learning.

Area of Science:

• Computational biology
• Bioinformatics
• Machine learning applications in life sciences

Background:

• Genome sequencing data is widely available, driving the popularity of machine learning (ML) for protein property prediction.
• Recurring issues in published research hinder understanding and replication of ML-based protein studies.
• A gap exists between biologists' domain knowledge and ML experts' methodological application to proteins.

Purpose of the Study:

• To bridge the knowledge gap between biologists and ML experts in protein property prediction.
• To provide clear guidelines for developing and reporting ML methods in computational biology.
• To improve the clarity, rigor, and reproducibility of ML-based biological research.

Main Methods:

• Analysis of recurring issues in published manuscripts and pre-prints.
• Identification of common pitfalls in applying ML to protein sequence data.
• Synthesis of best practices for clarity, rigor, and benchmark comparisons.

Main Results:

• Identified critical issues related to annotation clarity (source, metrics, definitions).
• Highlighted problems concerning methodological rigor (e.g., use of structural information, benchmark selection, statistical significance).
• Detailed specific recommendations for developers to avoid common errors.

Conclusions:

• Clear reporting of data sources, metrics, and definitions is crucial for ML studies.
• Maintaining methodological rigor, including avoiding hidden data sources and using appropriate comparisons, is essential.
• Adherence to these guidelines will enhance the understanding, replicability, and impact of ML applications in computational biology.