We show that each of our auxiliary tasks boosts learning of the embedding vectors, and that contrastive learning using Boost-RS outperforms attribute concatenation and multi-label learning.
In this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA), to address the graph matching problem.
Some interactions are attributed to natural selection and involve the enzyme's natural substrates.
We develop in this work a technique, Enzymatic Link Prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules.
Annotation results are in agreement to those obtained using other tools that utilize additional information in the form of spectral signatures.