Patterns In Big Data Bioinformatics : Understanding Complex Diseases With Interpretable Machine Learning

This article discusses the use of interpretable machine learning to analyze large omics data sets and identify patterns and associations among features. The authors developed a rule-based ML package, R.ROSETTA, and used it to analyze gene expression data in a case-control study of autism. They also applied this approach to merge multiple cohorts and identify potential interactions between genes. The results demonstrate the effectiveness of rule-based modeling in identifying complex relationships in genetic data.