Deakin University

About: Deakin University is a education organization based out in Burwood, Victoria, Australia. It is known for research contribution in the topics: Population & Context (language use). The organization has 12118 authors who have published 46470 publications receiving 1188841 citations. The organization is also known as: Deakin.

Highly Thermoconductive, Thermostable, and Super-Flexible Film by Engineering 1D Rigid Rod-Like Aramid Nanofiber/2D Boron Nitride Nanosheets.

Abstract: Polymer-based thermal management materials have many irreplaceable advantages not found in metals or ceramics, such as easy processing, low density, and excellent flexibility. However, their limited thermal conductivity and unsatisfactory resistance to elevated temperatures ( 100 MPa, 450 °C) enable effective thermal management for microelectrodes operating at temperatures beyond 200 °C.

GraphDTA: predicting drug-target binding affinity with graph neural networks.

Abstract: The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug–target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity. We show that graph neural networks not only predict drug–target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug–target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Availability of data and materials The proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post-hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523.

Toward a Complexity Theory of Information Systems Development

Abstract: Existing literature acknowledges Information Systems Development (ISD) to be a complex activity This complexity is magnified by the continuous changes in user requirements due to changing organizational needs in changing external competitive environments Research findings show that if this increasing complexity is not managed appropriately, information systems fail The paper thus, portrays the sources of complexity related to ISD and suggests the use of complexity theory as a frame of reference analyzing its implications on information system design and development to deal with the emergent nature of IS