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Detection of Anxiety from Brain Electroencephalogram (EEG) Signals
Marko, Július ; Shakil, Sadia (oponent) ; Malik, Aamir Saeed (vedoucí práce)
Anxiety affects human abilities, behavior, productivity, and quality of life. Anxiety keeps us safe as part of a system that helps to control and avert danger. However, this safety system can go wrong. When such impairment emerges, it can lead to depression and even suicide. This work aims to develop a novel method of anxiety detection from brain signals, in particular electroencephalography (EEG), a non-invasive and cost-effective screening method. The proposed method incorporates microstates, which were not previously utilized for anxiety detection. Additional features in the time and frequency domain are extracted. Finally, a machine learning classifier is trained and evaluated on these features, outperforming other existing methods.
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Prediction of the Effect of Mutation on Protein Solubility
Marko, Július ; Smatana, Stanislav (oponent) ; Hon, Jiří (vedoucí práce)
Protein solubility is a key problem in production of functional proteins. Prediction of the effect of mutation on protein solubility could save a lot of time and money, as it would provide in silico prediction of solubility enhancing mutations before performing deep mutational scanning in laboratory. In this work, new predictor of the effect of mutation on protein solubility SoluProtMut is introduced that is based on machine learning methods. Most of the existing predictors predict the effect from the amino acid sequence. In addition to the sequence, the tool presented in this work also uses the spatial structure of the protein, which can significantly increase it's accuracy.
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Prediction of the Effect of Mutation on Protein Solubility
Marko, Július ; Smatana, Stanislav (oponent) ; Hon, Jiří (vedoucí práce)
Protein solubility is a key problem in production of functional proteins. Prediction of the effect of mutation on protein solubility could save a lot of time and money, as it would provide in silico prediction of solubility enhancing mutations before performing deep mutational scanning in laboratory. In this work, new predictor of the effect of mutation on protein solubility SoluProtMut is introduced that is based on machine learning methods. Most of the existing predictors predict the effect from the amino acid sequence. In addition to the sequence, the tool presented in this work also uses the spatial structure of the protein, which can significantly increase it's accuracy.
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