ORCID Profile
0000-0002-0790-2846
Current Organisation
University of Technology Sydney
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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.
Simulation and Modelling | Pattern Recognition and Data Mining | Artificial Intelligence and Image Processing | Bioprocessing, Bioproduction and Bioproducts |
Expanding Knowledge in the Information and Computing Sciences | Human Biological Preventatives (e.g. Vaccines) | Human Pharmaceutical Treatments (e.g. Antibiotics)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Springer International Publishing
Date: 2019
Publisher: arXiv
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: arXiv
Date: 2018
Publisher: Elsevier BV
Date: 12-2021
Publisher: arXiv
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 06-2021
DOI: 10.1007/S10994-021-05992-X
Abstract: Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 18-07-2021
Publisher: ACM
Date: 20-08-2020
Publisher: American Society of Hematology
Date: 13-11-2019
DOI: 10.1182/BLOOD-2019-129519
Abstract: Background: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy and risk stratification based on genetic and clinical variables is standard practice. However, current models incorporating these factors accurately predict clinical outcomes for only 64-80% of patients and fail to provide clear treatment guidelines for patients with intermediate genetic risk. A plethora of prognostic gene expression signatures (PGES) have been proposed to improve outcome predictions but none of these have entered routine clinical practice and their role remains uncertain. Methods: To clarify clinical utility, we performed a systematic evaluation of eight highly-cited PGES i.e. Marcucci-7, Ng-17, Li-24, Herold-29, Eppert-LSCR-48, Metzeler-86, Eppert-HSCR-105, and Bullinger-133. We investigated their constituent genes, methodological frameworks and prognostic performance in four cohorts of non-FAB M3 AML patients (n= 1175). All patients received intensive anthracycline and cytarabine based chemotherapy and were part of studies conducted in the United States of America (TCGA), the Netherlands (HOVON) and Germany (AMLCG). Results: There was a minimal overlap of in idual genes and component pathways between different PGES and their performance was inconsistent when applied across different patient cohorts. Concerningly, different PGES often assigned the same patient into opposing adverse- or favorable- risk groups (Figure 1A: Rand index analysis RI=1 if all patients were assigned to equal risk groups and RI =0 if all patients were assigned to different risk groups). Differences in the underlying methodological framework of different PGES and the molecular heterogeneity between AMLs contributed to these low-fidelity risk assignments. However, all PGES consistently assigned a significant subset of patients into the same adverse- or favorable-risk groups (40%-70% Figure 1B: Principal component analysis of the gene components from the eight tested PGES). These patients shared intrinsic and measurable transcriptome characteristics (Figure 1C: Hierarchical cluster analysis of the differentially expressed genes) and could be prospectively identified using a high-fidelity prediction algorithm (FPA). In the training set (i.e. from the HOVON), the FPA achieved an accuracy of ~80% (10-fold cross-validation) and an AUC of 0.79 (receiver-operating characteristics). High-fidelity patients were dichotomized into adverse- or favorable- risk groups with significant differences in overall survival (OS) by all eight PGES (Figure 1D) and low-fidelity patients by two of the eight PGES (Figure 1E). In the three independent test sets (i.e. form the TCGA and AMLCG), patients with predicted high-fidelity were consistently dichotomized into the same adverse- or favorable- risk groups with significant differences in OS by all eight PGES. However, in-line with our previous analysis, patients with predicted low-fidelity were dichotomized into opposing adverse- or favorable- risk groups by the eight tested PGES. Conclusion: With appropriate patient selection, existing PGES improve outcome predictions and could guide treatment recommendations for patients without accurate genetic risk predictions (~18-25%) and for those with intermediate genetic risk (~32-35%). Figure 1 Hiddemann: Celgene: Consultancy, Honoraria Roche: Consultancy, Honoraria, Research Funding Bayer: Research Funding Vector Therapeutics: Consultancy, Honoraria Gilead: Consultancy, Honoraria Janssen: Consultancy, Honoraria, Research Funding. Metzeler:Celgene: Honoraria, Research Funding Otsuka: Honoraria Daiichi Sankyo: Honoraria. Pimanda:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Beck:Gilead: Research Funding.
Publisher: arXiv
Date: 2021
Publisher: Elsevier BV
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: arXiv
Date: 2022
Publisher: Elsevier BV
Date: 10-2020
Publisher: Elsevier BV
Date: 04-2020
Publisher: arXiv
Date: 2020
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 09-2020
Publisher: Public Library of Science (PLoS)
Date: 26-08-2022
DOI: 10.1371/JOURNAL.PONE.0273609
Abstract: Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements.
Publisher: arXiv
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Science and Business Media LLC
Date: 04-08-2021
Publisher: IEEE
Date: 12-01-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: arXiv
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 11-2019
Publisher: arXiv
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 12-2018
Publisher: arXiv
Date: 2016
Publisher: arXiv
Date: 2020
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2018
Publisher: Public Library of Science (PLoS)
Date: 25-06-2021
DOI: 10.1371/JOURNAL.PONE.0253822
Abstract: The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the centre-node in an open triad. In contrast, the recently proposed closure coefficient measures triangle formation from an end-node perspective and has been proven to be a useful feature in network analysis. Here, we extend it by proposing the directed closure coefficient that measures the formation of directed triangles. By distinguishing the direction of the closing edge in building triangles, we further introduce the source closure coefficient and the target closure coefficient. Then, by categorising particular types of directed triangles (e.g., head-of-path), we propose four closure patterns. Through multiple experiments on 24 directed networks from six domains, we demonstrate that at network-level, the four closure patterns are distinctive features in classifying network types, while at node-level, adding the source and target closure coefficients leads to significant improvement in link prediction task in most types of directed networks.
Start Date: 06-2019
End Date: 03-2025
Amount: $600,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2022
End Date: 07-2027
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded Activity