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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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Segmentace míšního kanálu a meziobratlových plotének v MRI datech
Koban, Martin ; Odstrčilík, Jan (oponent) ; Jakubíček, Roman (vedoucí práce)
Práca sa venuje vývoju metódy pre segmentáciu spinálneho kanálu a intervertebrálnych diskov v objemových MRI dátach. Cieľom je čo najvyšší stupeň automatizácie postupu a presnosť umožňujúca spoľahlivé kvantitatívne hodnotenie výsledkov. Základ segmentačného algoritmu tvorí model náhodnej prechádzky v kombinácii so špecifickou metódou aktívnych kontúr formulovanou prostredníctvom konceptu level set. Navrhnutý postup je testovaný na databáze trojrozmerných T2-váhovaných MRI snímok, ktorej súčasťou je aj referenčná manuálna segmentácia intervertebrálnych diskov.
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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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3d Segmentation Of The Spinal Canal And Intervertebral Discs In Mri Data
Koban, Martin
The concern of this work is development of the method for the spinal canal and intervertebral discs (IVD) segmentation in volume MRI data. The primary aim is to achieve the highest possible level of automation and accuracy allowing for reliable quantitative evaluation of the results. The algorithm is based on the random walk model in combination with a specific active contour method formulated through level set concept. The proposed approach is tested using a database of 3D T2-weighted MR images, which also contains referential manual segmentation of IVD.
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Segmentace míšního kanálu a meziobratlových plotének v MRI datech
Koban, Martin ; Odstrčilík, Jan (oponent) ; Jakubíček, Roman (vedoucí práce)
Práca sa venuje vývoju metódy pre segmentáciu spinálneho kanálu a intervertebrálnych diskov v objemových MRI dátach. Cieľom je čo najvyšší stupeň automatizácie postupu a presnosť umožňujúca spoľahlivé kvantitatívne hodnotenie výsledkov. Základ segmentačného algoritmu tvorí model náhodnej prechádzky v kombinácii so špecifickou metódou aktívnych kontúr formulovanou prostredníctvom konceptu level set. Navrhnutý postup je testovaný na databáze trojrozmerných T2-váhovaných MRI snímok, ktorej súčasťou je aj referenčná manuálna segmentácia intervertebrálnych diskov.
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