KEDRI - the Knowledge Engineering and Discovery Research Institute

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https://hdl.handle.net/10292/552
KEDRI - the Knowledge Engineering and Discovery Research Institute of Auckland University of Technology was established in June 2002 and since then has been developing novel information processing methods, technologies and their applications to enhance discoveries across different areas of science and engineering. The methods are mainly based on principles from Nature, such as brain information processing, evolution, genetics, quantum physics.

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Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Author "Budhraja, Sugam"

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    Filter and Wrapper Stacking Ensemble (FWSE): A Robust Approach for Reliable Biomarker Discovery in High-Dimensional Omics Data
    (Oxford University Press (OUP), 2023) Budhraja, Sugam; Doborjeh, Maryam; Singh, Balkaran; Tan, Samuel; Doborjeh, Zohreh; Lai, Edmund; Merkin, Alexander; Lee, Jimmy; Goh, Wilson; Kasabov, Nikola
    Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.
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    Investigation of Social and Cognitive Predictors in Non-transition Ultra-high-risk' Individuals for Psychosis Using Spiking Neural Networks
    (Springer Science and Business Media LLC, 2023-02-15) Doborjeh, Zohreh; Doborjeh, Maryam; Sumich, Alexander; Singh, Balkaran; Merkin, Alexander; Budhraja, Sugam; Goh, Wilson; Lai, Edmund M-K; Williams, Margaret; Tan, Samuel; Lee, Jimmy; Kasabov, Nikola
    Finding predictors of social and cognitive impairment in non-transition Ultra-High-Risk individuals (UHR) is critical in prognosis and planning of potential personalised intervention strategies. Social and cognitive functioning observed in youth at UHR for psychosis may be protective against transition to clinically relevant illness. The current study used a computational method known as Spiking Neural Network (SNN) to identify the cognitive and social predictors of transitioning outcome. Participants (90 UHR, 81 Healthy Control (HC)) completed batteries of neuropsychological tests in the domains of verbal memory, working memory, processing speed, attention, executive function along with social skills-based performance at baseline and 4 × 6-month follow-up intervals. The UHR status was recorded as Remitters, Converters or Maintained. SNN were used to model interactions between variables across groups over time and classify UHR status. The performance of SNN was examined relative to other machine learning methods. Higher interaction between social and cognitive variables was seen for the Maintained, than Remitter subgroup. Findings identified the most important cognitive and social variables (particularly verbal memory, processing speed, attention, affect and interpersonal social functioning) that showed discriminative patterns in the SNN models of HC vs UHR subgroups, with accuracies up to 80%; outperforming other machine learning models (56-64% based on 18 months data). This finding is indicative of a promising direction for early detection of social and cognitive impairment in UHR individuals that may not anticipate transition to psychosis and implicate early initiated interventions to stem the impact of clinical symptoms of psychosis.
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