Feature engineered embeddings for classification of molecular data.

The classification of molecules is of particular importance to the drug discovery process and several other use cases. Data in this domain can be partitioned into structural and sequence/text data. Several techniques such as deep learning are able to classify molecules and predict their functions using both types of data.

Quantifying rehabilitation risks for surface-strip coal mines using a soil compaction Bayesian network in South Africa and Australia: To demonstrate the R AIN Framework.

Environmental information is acquired and assessed during the environmental impact assessment process for surface-strip coal mine approval. However, integrating these data and quantifying rehabilitation risk using a holistic multidisciplinary approach is seldom undertaken. We present a rehabilitation risk assessment integrated network (R AIN™) framework that can be applied using Bayesian networks (BNs) to integrate and quantify such rehabilitation risks.