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models (e.g. tumour progression, tumour-drug sensitivity, survivability) by integrating multiple and heterogeneous data with associative data mining and ensemble learning methods.
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short-(Illumina) and long-read sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes. Assoc. Prof. David Dowe will be the primary
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extract events and mine knowledge from existing unstructured/structured data, and exploit the knowledge via neuro-symbolic reasoning for crime prevention (eg -sexual assaults), especially when there is no
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for predictive analytics that incorporate modelling, machine learning, and data mining, we are building, analysing and modelling an individual’s baseline health profile against thousands (eventually millions
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techniques in bacterial genomics, including both short- (Illumina) and long-read sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up
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in the primary journal for time series classification research, Data Mining and Knowledge Discovery[7]; and our algorithm Rocket has been independently assessed as ‘the most important recent
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Edwards and D L Dowe (1998). "Single factor analysis in MML mixture modelling ", pp96 -109 , 2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD98), Lecture Notes in Artificial