Protein solubility prediction using CNN and CNN-LSTM hybrid models – Ekaterina SYSOYKOVA
Ekaterina SYSOYKOVA
Bachelor's thesis
Protein solubility prediction using CNN and CNN-LSTM hybrid models
Protein solubility prediction using CNN and CNN-LSTM hybrid models
Abstract:
Protein solubility prediction based on the raw amino acid sequence using three machine/deep learning techniques: RF, CNN, and a hybrid CNN-biLSTM model. Assessment of the performance difference between the models with evaluation metrics and statistical significance tests.Abstract:
Protein solubility prediction based on the raw amino acid sequence using three machine/deep learning techniques: RF, CNN, and a hybrid CNN-biLSTM model. Assessment of the performance difference between the models with evaluation metrics and statistical significance tests.
Language used: English
Date on which the thesis was submitted / produced: 20. 1. 2022
Thesis defence
- Supervisor: Mag. Günter Klambauer, Ph.D.
Citation record
ISO 690-compliant citation record:
SYSOYKOVA, Ekaterina. \textit{Protein solubility prediction using CNN and CNN-LSTM hybrid models}. Online. Bachelor's thesis. České Budějovice: University of South Bohemia in České Budějovice, Faculty of Science. 2022. Available from: https://theses.cz/id/mqi3ao/.
The right form of listing the thesis as a source quoted
SYSOYKOVA, Ekaterina. Protein solubility prediction using CNN and CNN-LSTM hybrid models. České Budějovice, 2022. bakalářská práce (Bc.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
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Institution archiving the thesis and making it accessible: JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH, Přírodovědecká fakultaUNIVERSITY OF SOUTH BOHEMIA IN ČESKÉ BUDĚJOVICE
Faculty of ScienceBachelor programme / field:
Applied Informatics / Bioinformatics
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