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Title: Using topological data analysis to assess the quality of imputed missing data
Abstract: As the number of dimensions increases, big datasets from precision medicine research studies can exhibit complex shapes and unexpected behaviours. The statistical analysis of such data necessitates sophisticated analytical methods capable of capitalizing on the high dimension of these datasets. This talk will present novel methods of applying TDA to devise a unique approach for assessing the quality of data imputation for missing values. The method establishes a pipeline that combine TDA with permutation testing to identify differences in topological data structures among datasets. This provides valuable information for tailoring the selection of missing data imputation strategies.
Biography: Mr Yiyang Ge. Yiyang is a last year PhD student (thesis submitted) at the Biostatistics and Health Informatics Department at King's College London, under Prof Daniel Stahl and Dr Raquel Iniesta. His thesis is an investigation of topological techniques that can improve the analysis or large biomedical datasets by extracting relevant information on big data structure and shape.