Structure-based detection of protein interaction sites with machine learning
| Název práce v češtině: | Využití strojového učerní k detekci interakčních míst proteinové struktury |
|---|---|
| Název v anglickém jazyce: | Structure-based detection of protein interaction sites with machine learning |
| Klíčová slova: | bioinformatika|molekulární interakce|strukutrní bioinformatika|strojové učení |
| Klíčová slova anglicky: | bioinformatics|molecular interaction|structural bioinformatics|machine learning |
| Akademický rok vypsání: | 2023/2024 |
| Typ práce: | disertační práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra softwarového inženýrství (32-KSI) |
| Vedoucí / školitel: | doc. RNDr. David Hoksza, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 20.03.2024 |
| Datum zadání: | 20.03.2024 |
| Datum potvrzení stud. oddělením: | 21.03.2024 |
| Zásady pro vypracování |
| Proteins, the primary building blocks of living systems, function by interacting with other molecules such as DNAs, RNAs, small molecules, or other proteins. Although at the most elementary level, the interactions are driven by the same physical forces, on a higher level, where the environment is captured by more abstract concepts such as amino acid type, shape, or even evolutionary conservation, the different interaction types are associated with different properties. The thesis aims to study these higher-level concepts and explore representations and machine learning approaches to capture these concepts and use them to detect interaction sites on yet unseen protein structures. |
| Seznam odborné literatury |
| [1] Gu, J., & Bourne, P. E. (Eds.). (2009). Structural bioinformatics (Vol. 44). John Wiley & Sons.
[2] Durbin, R., Eddy, S. R., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge university press. [3] Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer. [4] Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. [5] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4-24. |