Computational scoring and experimental evaluation of enzymes generated by neural networks | Nature Biotechnology
In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. Researchers evaluate a set of 20 diverse computational metrics to assess the quality of enzyme sequences produced by three contrasting generative models: ancestral sequence reconstruction, a generative adversarial network and a protein language model.
Measuring, visualizing, and diagnosing reference bias with biastools | Genome Biology
Many bioinformatics methods seek to reduce reference bias, but no methods exist to comprehensively measure it. Biastools analyzes and categorizes instances of reference bias.
Causal machine learning for predicting treatment outcomes | Nature Medicine
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. In this Perspective, the authors discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps.