Representation Learning on Graphs: Applications and Innovations across the Pharma value chain.
Low dimensional representation learning of data that are naturally graph structured have gained popularity over recent years with varied applications. Graph neural networks, by design, are capable of integrating node informations, topological structure and relationship between data elements leading to increased accuracy of representing data from non-euclidian domains. Traditional deep learning representation typically struggles with tasks which require representing complex interdependence between data objects due to their inherent design of projecting embeddings in Euclidian space leading to the aggregation of relationships. GNNs however come in various flavors, graph convolution, temporal graphs, auto-encoders, transformers, spatio-temporal and others, which make GNNs quite versatile to tackle a diversity of use-cases frequently encountered in a large industrial setting. Here, we showcase our experiments using GNNs for low dimensional representation learning and using them to tackle various use cases across the pharma value chain. We compare and contrast these methods with more traditional ones and also highlight how such applications lead to generation of insights, and predictions with direct application towards various use cases.
Srayanta is a Data Scientist and computational biologist with 10 years research experience, having worked a diverse spectrum of problems including predictive modeling and operations research.
He has extensive experience in machine learning methods and is a specialist in stochastic simulations, deep learning and decision trees.
His roles have included leading his team towards end-to-end data science solutions, achieved strategic milestones and drove adoption
