Synergy Between Embedding and Protein Functional Association Networks for Drug Label Prediction Using Harmonic Function

Semi-Supervised Learning (SSL)is an approach to machine learning that makes use of unlabeled data for training with a small amount of labeled data. In the context of molecular biology and pharmacology, one can take advantage of unlabeled data. For instance, to identify drugs and targets where a few genes are known to be associated with a specific target for drugs and considered as labeled data. Labeling the genes requires laboratory verification and validation. This process is usually very time consuming and expensive. Thus, it is useful to estimate the functional role of drugs from unlabeled data using computational methods. To develop such a model, we used openly available data resources to create (i)drugs and genes, (ii)genes and disease, bipartite graphs. We constructed the genetic embedding graph from the two bipartite graphs using Tensor Factorization methods. We integrated the genetic embedding graph with the publicly available protein functional association network. Our results show the usefulness of the integration by effectively predicting drug labels.

Automated summary

Semi-Supervised Learning utilizes unlabeled data along with a small amount of labeled data for training in machine learning. In this study, computational methods were used to estimate the functional role of drugs from unlabeled data, demonstrating the effectiveness of integrating genetic embedding graphs with protein functional association networks in predicting drug labels.