ORCID Profile
0000-0002-0350-3899
Current Organisation
CSIRO
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Cold Spring Harbor Laboratory
Date: 22-02-2018
DOI: 10.1101/269720
Abstract: AlignmentViewer is multiple sequence alignment viewer for protein families with flexible visualization, analysis tools and links to protein family databases. It is directly accessible in web browsers without the need for software installation, as it is implemented in JavaScript, and does not require an internet connection to function. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, facilitating the computation of protein 3D structures and the detection of functionally constrained interactions. AlignmentViewer is open source software under the MIT license. The viewer is at alignmentviewer.org and the source code, documentation and issue tracking, for co-development, are at fci/alignmentviewer alignment.viewer@gmail.com , reaches all authors
Publisher: Springer Science and Business Media LLC
Date: 02-10-2020
DOI: 10.1038/S41467-020-18682-4
Abstract: We present the Danish Disease Trajectory Browser (DTB), a tool for exploring almost 25 years of data from the Danish National Patient Register. In the dataset comprising 7.2 million patients and 122 million admissions, users can identify diagnosis pairs with statistically significant directionality and combine them to linear disease trajectories. Users can search for one or more disease codes (ICD-10 classification) and explore disease progression patterns via an array of functionalities. For ex le, a set of linear trajectories can be merged into a disease trajectory network displaying the entire multimorbidity spectrum of a disease in a single connected graph. Using data from the Danish Register for Causes of Death mortality is also included. The tool is disease-agnostic across both rare and common diseases and is showcased by exploring multimorbidity in Down syndrome (ICD-10 code Q90) and hypertension (ICD-10 code I10). Finally, we show how search results can be customized and exported from the browser in a format of choice (i.e. JSON, PNG, JPEG and CSV).
Publisher: Springer Science and Business Media LLC
Date: 06-05-2021
DOI: 10.1038/S41598-021-89225-0
Abstract: Diabetic retinopathy (DR) is a leading cause of blindness and affects millions of people throughout the world. Early detection and timely checkups are key to reduce the risk of blindness. Automated grading of DR is a cost-effective way to ensure early detection and timely checkups. Deep learning or more specifically convolutional neural network (CNN)—based methods produce state-of-the-art performance in DR detection. Whilst CNN based methods have been proposed, no comparisons have been done between the extracted image features and their clinical relevance. Here we first adopt a CNN visualization strategy to discover the inherent image features involved in the CNN’s decision-making process. Then, we critically analyze those features with respect to commonly known pathologies namely microaneurysms, hemorrhages and exudates, and other ocular components. We also critically analyze different CNNs by considering what image features they pick up during learning to predict and justify their clinical relevance. The experiments are executed on publicly available fundus datasets (EyePACS and DIARETDB1) achieving an accuracy of 89 ~ 95% with AUC, sensitivity and specificity of respectively 95 ~ 98%, 74 ~ 86%, and 93 ~ 97%, for disease level grading of DR. Whilst different CNNs produce consistent classification results, the rate of picked-up image features disagreement between models could be as high as 70%.
Publisher: American Medical Association (AMA)
Date: 07-2020
Publisher: Elsevier BV
Date: 2023
Publisher: Cold Spring Harbor Laboratory
Date: 31-08-2022
DOI: 10.1101/2022.08.30.22279381
Abstract: Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned ICU admission. Classical severity scores have been developed typically using relatively few patient features, especially for intensive care. Recently, deep learning-based models demonstrated better in idualized risk assessments compared to classic risk scores such as SOFA and NEWS, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-st ed data from electronic health records. We used medical history data, biochemical measurements, and the clinical notes from all patients admitted to non-intensive care units in 12 hospitals in Denmark’s Capital Region and Region Zealand during 2011-2016. Data from a total of 852,620 patients and 2,241,849 admissions were used to predict the composite outcome of unplanned ICU transfer and in-hospital death at different time points after admission to general departments. We subsequently examined feature interpretations of the models. The best model used all data modalities with an assessment rate of 6 hours and a prediction window of 14 days, with an AUPRC of 0.287 and AUROC of 0.898. These performances are comparable to the current state of the art and make the model suitable for further prospective validation as a risk assessment tool in a clinical setting.
Publisher: F1000 Research Ltd
Date: 27-03-2020
DOI: 10.12688/F1000RESEARCH.22242.1
Abstract: AlignmentViewer is a web-based tool to view and analyze multiple sequence alignments of protein families. The particular strengths of AlignmentViewer include flexible visualization at different scales as well as analysis of conservation patterns and of the distribution of proteins in sequence space. The tool is directly accessible in web browsers without the need for software installation. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, e.g. via EVcouplings.org.
Publisher: Public Library of Science (PLoS)
Date: 09-06-2023
DOI: 10.1371/JOURNAL.PDIG.0000116
Abstract: Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned admission to the intensive care unit (ICU). Classical severity scores have been developed typically using relatively few patient features. Recently, deep learning-based models demonstrated better in idualized risk assessments compared to classic risk scores, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-st ed data from electronic health records. We developed a deep learning model based on embedded text from multiple data sources and recurrent neural networks to predict the risk of the composite outcome of unplanned ICU transfer and in-hospital death. The risk was assessed at regular intervals during the admission for different prediction windows. Input data included medical history, biochemical measurements, and clinical notes from a total of 852,620 patients admitted to non-intensive care units in 12 hospitals in Denmark’s Capital Region and Region Zealand during 2011–2016 (with a total of 2,241,849 admissions). We subsequently explained the model using the Shapley algorithm, which provides the contribution of each feature to the model outcome. The best model used all data modalities with an assessment rate of 6 hours, a prediction window of 14 days and an area under the receiver operating characteristic curve of 0.898. The discrimination and calibration obtained with this model make it a viable clinical support tool to detect patients at higher risk of clinical deterioration, providing clinicians insights into both actionable and non-actionable patient features.
Publisher: F1000 Research Ltd
Date: 15-10-2020
DOI: 10.12688/F1000RESEARCH.22242.2
Abstract: AlignmentViewer is a web-based tool to view and analyze multiple sequence alignments of protein families. The particular strengths of AlignmentViewer include flexible visualization at different scales as well as analysis of conservation patterns and of the distribution of proteins in sequence space. The tool is directly accessible in web browsers without the need for software installation. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, e.g. via EVcouplings.org.
No related grants have been discovered for Roc Reguant.