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Machine learning models for quantifying phenotypic signatures of cancer cells based on transcriptomic and epigenomic data
Koban, Martin ; PhD, Florian Halbritter, (oponent) ; Mehnen, Lars (vedoucí práce)
Since the advent of techniques capable of rapid acquisition of genomic data, it is one of the key challenges for researchers to interpret the results of such experiments in meaningful biological terms. In this work, we aim to exploit knowledge hidden in well-characterised transcriptomic and epigenomic data from publicly available sources to aid this interpretation. An integrated resource of chromatin accessibility data (from DNase-seq and ATAC-seq experiments) was created and pre-processed for downstream analyses, complemented by collections of public gene expression (RNA-seq) profiles. These datasets were used for training machine learning classifiers with two primary purposes. Firstly, for augmenting sample annotations by predicting undefined metadata labels in the training datasets. Secondly, for annotation of poorly characterised, unseen data to examine generalisation ability of the constructed models. We demonstrated that biologically relevant information was captured by the trained classifiers while technical artefacts were minimised. Thus, we validated that the proposed supervised machine learning approach can contribute to clarifying contents of cryptic transcriptomic and epigenomic datasets, particularly from the field of cancer research.
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Machine learning models for quantifying phenotypic signatures of cancer cells based on transcriptomic and epigenomic data
Koban, Martin ; PhD, Florian Halbritter, (oponent) ; Mehnen, Lars (vedoucí práce)
Since the advent of techniques capable of rapid acquisition of genomic data, it is one of the key challenges for researchers to interpret the results of such experiments in meaningful biological terms. In this work, we aim to exploit knowledge hidden in well-characterised transcriptomic and epigenomic data from publicly available sources to aid this interpretation. An integrated resource of chromatin accessibility data (from DNase-seq and ATAC-seq experiments) was created and pre-processed for downstream analyses, complemented by collections of public gene expression (RNA-seq) profiles. These datasets were used for training machine learning classifiers with two primary purposes. Firstly, for augmenting sample annotations by predicting undefined metadata labels in the training datasets. Secondly, for annotation of poorly characterised, unseen data to examine generalisation ability of the constructed models. We demonstrated that biologically relevant information was captured by the trained classifiers while technical artefacts were minimised. Thus, we validated that the proposed supervised machine learning approach can contribute to clarifying contents of cryptic transcriptomic and epigenomic datasets, particularly from the field of cancer research.
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