Machine learning is a powerful tool in computational biology, enabling the analysis of a wide range of biomedical data such as genomic sequences and biological imaging. But when researchers use machine learning in computational biology, understanding model behavior remains crucial for uncovering the underlying biological mechanisms in health and disease. In a recent article in Nature Methods , researchers at Carnegie Mellon University's School of Computer Science propose guidelines that outline pitfalls and opportunities for using interpretable machine learning methods to tackle computational biology problems.
The Perspectives article, "Applying Interpretable Machine Learning in Computational Biology -; Pitfalls, Recommendations and Opportunities for New Developments," is featured in the journal's August special issue on AI. Interpretable machine learning has generated significant excitement as machine learning and artificial intelligence tools are being applied to increasingly important problems. As these models grow in complexity, there is great promise not only in developing highly predictive models but also in creating tools that help end users understand how and why these models make certain predictions.
However, it is crucial to acknowledge that interpretable machine learning has yet to deliver turnkey solutions to this interpretability problem." Ameet Talwalkar, associate professor in CMU's Machine Learning Department (MLD) The paper is a collaboration between doctoral.