Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology.

While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery.The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas: (1) application of convolutional neural networks to predict ligand-protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also: (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learning in -omics research and drug discovery.

Environmental and Anthropogenic Impacts on Avifaunal Assemblages in an Urban Parkland, 1976 to 2007.

Urban environments are unique, rapidly changing habitats in which almost half of the world's human population resides. The effects of urbanisation, such as habitat (vegetation) removal, pollution and modification of natural areas, commonly cause biodiversity loss. Long-term ecological monitoring of urban environments is vital to determine the composition and long-term trends of faunal communities. This paper provides a detailed view of long-term changes in avifaunal assemblages of the Adelaide City parklands and discusses the anthropogenic and environmental factors that contributed to the changes between 1976 and 2007. The Adelaide City parklands (ACP) comprise 760 ha of land surrounding Adelaide's central business district. Naturalist Robert Whatmough completed a 32-year survey of the ACP to determine the structure of the urban bird community residing there. Annual species richness and the abundance of birds in March and September months were analysed. Linear regression analysis was applied to species richness and abundance data of each assemblage. Resident parkland birds demonstrated significant declines in abundance. Native and introduced species also exhibited long-term declines in species richness and abundance throughout the 32-year period. Cycles of varying time periods indicated fluctuations in avian biodiversity demonstrating the need for future monitoring and statistical analyses on bird communities in the Adelaide City parklands.