ORCID Profile
0000-0003-2247-850X
Current Organisation
Deakin University - Geelong Campus at Waurn Ponds
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Cognitive Science | Medical Devices | Machine learning not elsewhere classified | Computer Software | Data security and protection | Health Information Systems (incl. Surveillance) | Pattern Recognition and Data Mining | Cybersecurity and privacy | Software Engineering | Knowledge Representation and Machine Learning |
Application Software Packages (excl. Computer Games) | Behaviour and Health | Expanding Knowledge in the Information and Computing Sciences | Integrated Circuits and Devices |
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 04-2022
Publisher: MDPI AG
Date: 06-06-2022
DOI: 10.3390/MET12060975
Abstract: The K mann and Wagner numerical model was adapted in MATLAB to predict the precipitation and growth of Al3Sc precipitates as a function of starting concentration and heat-treatment steps. This model was then expanded to predict the strengthening in alloys using calculated average precipitate number density, radius, etc. The calibration of this model was achieved with Bayesian optimization, and the model was verified against experimentally gathered hardness data. An analysis of the outputs from this code allowed the development of optimal heat treatments, which were validated experimentally and proven to result in higher final strengths than were previously observed. Bayesian optimization was also used to predict the optimal heat-treatment temperatures in the case of limited heat-treatment times.
Publisher: IEEE
Date: 21-08-2022
Publisher: American Chemical Society (ACS)
Date: 09-04-2020
Publisher: MDPI AG
Date: 24-02-2023
DOI: 10.3390/ANTIBIOTICS12030463
Abstract: Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal–ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes’ root-mean-square deviation (RMSD) and the program’s internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 AD4 Vina RDOCK RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket’s high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative’s tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. c aigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.
Publisher: Cold Spring Harbor Laboratory
Date: 06-12-2022
DOI: 10.1101/2022.12.05.22283129
Abstract: Meta-analytic evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI) driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multi-arm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. The Vibe Up study is a pragmatically-oriented, decentralised AI-adaptive group sequential randomised controlled trial (RCT) comparing the effectiveness of one of three brief, two week digital self-guided interventions (mindfulness, physical activity, or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (DASS-21 total score) from pre-intervention to post-intervention. Secondary outcomes include change in depression, anxiety, and stress (measured by DASS-21 subscales) from pre-intervention to post-intervention. Planned contrasts will compare the four groups (i.e., the three intervention and control) using self-reported psychological distress at pre-specified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice [1] was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). The trial is registered with the Australian New Zealand Clinical Trials Registry (AC-TRN12621001223820). The study addresses an important clinical question using novel, advanced methods The trial uses short-duration interventions designed to improve coping responses to transient stressors, which addresses the most common needs of university students A value of information analysis is included to compare the value of the new trial methods with traditionalapproaches Digital phenotyping is used to explore smartphone sensor information with clinical outcomes More than 12 mini-trials might be required to determine the ranking for the interventions The interventions may prove to be of the same level of effectiveness for each level of severity Interventions other than those examined in this study, such as CBT, may be more effective and remain untested The methodology assumes that the three digital interventions are configured to deliver similar doses and/or have approximate fidelity with standard methods
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 20-01-2020
DOI: 10.1038/S41746-019-0205-Y
Abstract: Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical activity (PA) with their patients. Interventions were developed based on a causal loop diagram with 26 GPs across 13 clinics in Geelong, Australia. GPs prioritised eight from more than 80 potential interventions to increase GP discussion of PA with patients. Following a 2-week baseline, a multi-arm bandit algorithm was used to assign optimal strategies to GP clinics with the target outcome being GP PA discussion rates. The algorithm was updated weekly and the process iterated until the more promising strategies emerged (a duration of seven weeks). The top three performing strategies were continued for 3 weeks to improve the power of the hypothesis test of effectiveness for each strategy compared to baseline. GPs recorded a total of 11,176 conversations about PA. GPs identified 15 factors affecting GP PA discussion rates with patients including GP skills and awareness, fragmentation of care and fear of adverse outcomes. The two most effective strategies were correctly identified within seven weeks of the algorithm-based assignment of strategies. These were clinic reception staff providing PA information to patients at check in and PA screening questionnaires completed in the waiting room. This study demonstrates an efficient way to test and identify optimal strategies from multiple possible solutions.
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: JMIR Publications Inc.
Date: 16-12-2016
DOI: 10.2196/JMIR.5870
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 12-2023
Publisher: Destech Publications, Inc.
Date: 09-05-2022
DOI: 10.12783/BALLISTICS22/36177
Abstract: We present a Bayesian optimisation methodology intended to support a human expert in the design of armour systems for which limited prior knowledge/data exists and within a limiting, pre-defined experimental budget. We apply the methodology to design an armour configuration consisting of multiple plates, with multiple materials, at varying orientations and spacing, for protection against 12.7 mm APM2 and 20 mm FSP threats. The full-factorial design matrix for the defined solution space exceeds 17,500 possible solutions. With an objective to minimise system weight, we identify a solution within 102 ballistic tests (44 design iterations) that provides a weight reduction of 11.4% over expert-designed reference configurations and a mass efficiency of 1.5 relative to a monolithic RHA Class 1. The value of the demonstrated methodology is expected to increase with increasing armour (or threat) complexity.
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Elsevier BV
Date: 10-2020
Publisher: Springer Science and Business Media LLC
Date: 22-09-2021
Publisher: Elsevier BV
Date: 11-2020
Publisher: Proceedings of the National Academy of Sciences
Date: 17-03-2022
Abstract: To move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively s le the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes’ whole s ling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active s ling theory. It shows how photomechanical photoreceptor microsaccades enable Drosophila superresolution three-dimensional vision and proposes neural computations for accurately predicting these flies’ depth-perception dynamics, limits, and visual behaviors.
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 10-2022
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 09-2022
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 28-06-2022
Abstract: Random Fourier features (RFF) are a popular set of tools for constructing low-dimensional approximations of translation-invariant kernels, allowing kernel methods to be scaled to big data. Apart from their computational advantages, by working in the spectral domain random Fourier features expose the translation invariant kernel as a density function that may, in principle, be manipulated directly to tune the kernel. In this paper we propose selecting the density function from a reproducing kernel Hilbert space to allow us to search the space of all translation-invariant kernels. Our approach, which we call tuned random features (TRF), achieves this by approximating the density function as the RKHS-norm regularised least-squares best fit to an unknown ``true'' optimal density function, resulting in a RFF formulation where kernel selection is reduced to regularised risk minimisation with a novel regulariser. We derive bounds on the Rademacher complexity for our method showing that our random features approximation method converges to optimal kernel selection in the large N,D limit. Finally, we prove experimental results for a variety of real-world learning problems, demonstrating the performance of our approach compared to comparable methods.
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer Science and Business Media LLC
Date: 05-08-2021
DOI: 10.1186/S13040-021-00263-W
Abstract: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient. We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from in idual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 in idual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses. Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use in idualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for in idual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2021
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by s ling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be altered to better suit the problem without compromising a bound on the function's Bayesian regret. We also provide results showing that our method achieves better performance than GP-UCB in a range of real-world and synthetic problems.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Springer Science and Business Media LLC
Date: 14-08-2021
Publisher: IEEE
Date: 10-01-2021
Publisher: Springer Nature Switzerland
Date: 2023
Location: Australia
Location: India
Start Date: 2021
End Date: 2024
Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 2021
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2021
End Date: 01-2024
Amount: $361,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2023
Amount: $440,145.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2018
End Date: 10-2023
Amount: $2,962,655.00
Funder: Australian Research Council
View Funded Activity