Recent Posts

PeTriBERT : Augmenting BERT with tridimensional encoding for inverse protein folding and design (bioRxiv)

The AlphaFoldDB database recently made headlines by predicting the three-dimensional protein structure of millions of proteins based on their primary amino acid sequence. Most of these models remain to be tested, but it’s a place to start. Here, Dumortier et al. add an important complementary tool…

Machine learning algorithms predict soil seed bank persistence from easily available traits (Appl. Veg. Sci.)

Knowing whether a given plant species forms a persistent soil seed bank is essential to understanding its ecological dynamics, yet soil seed bank studies are both labor-intensive and time-consuming. One could argue that models should help predict this. Still, seed persistence in the soil is shaped by…

Using ‘Machine Learning’ to predict crop behaviour (Nature Plants)

Machine Learning (ML) is described by Wikipedia as ‘the study of computer algorithms that can improve automatically through experience and by the use of data’. In agronomy, machine learning can predict important crop traits (including yield) by analysing the large datasets such as data generated…