Detail projektu

Computer-aided approach for gene prediction based on metabolite genome-wide association studies in ecology and biotechnology

Období řešení: 01.02.2021 — 31.01.2023

Zdroje financování

Evropská unie - Interní grantová soutěž

- plně financující (2021-02-01 - 2023-01-31)

O projektu

The next generation genetic prediction is based on metabolite genome-wide association studies (mGWAS). The mGWAS approach provides the relationship between genetic factors and metabolome. Currently, important ecological issues are climate change, pollution etc. All these problems impact the change of environment. While simple observation reveals only phenotype changes, changes in genotypes of organisms can be captured using mGWAS and further utilized in industrial ecology and biotechnology by using engineered organisms.

Popis anglicky
The aim of the project is to create a competition for student research grants and its pilot verification. The creation of a new competition will contribute to the development of cross-sectional skills of doctoral students, and thus acquire competencies for work in science and research in the future and increase their success in submitting scientific projects to national and international competitions.

Označení

FEKT-K-21-6878

Originální jazyk

čeština

Řešitelé

Schwarzerová Jana, Ing. et Ing., MSc - hlavní řešitel
Musilová Jana, Ing., Ph.D. - spoluřešitel
Nohel Michal, Ing. et Ing. - spoluřešitel

Útvary

Fakulta elektrotechniky a komunikačních technologií
- příjemce (01.02.2021 - 31.01.2023)
Ústav biomedicínského inženýrství
- spolupříjemce (01.02.2021 - 31.01.2023)

Výsledky

SCHWARZEROVÁ, J. Metabolite Genome-wide Association Studies of Arabidopsis Thaliana. In Proceedings of the 27th Conference STUDENT EEICT 2021 selected papers. 1. Brno: Brno University of Technology, Faculty of Electrical Engineering and Communication, 2021. p. 41-44. ISBN: 978-80-214-5943-4.
Detail

SCHWARZEROVÁ, J.; BAJGER, A.; PIERDOU, I.; POPELINSKY, L.; SEDLÁŘ, K.; WECKWERTH, W. An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Institute of Electrical and Electronics Engineers Inc., 2021. p. 3075-3082. ISBN: 978-1-6654-0126-5.
Detail

SCHWARZEROVÁ, J.; NEMČEKOVÁ, P.; PIERDIES, I.; NOHEL, M.; CHMELÍK, J.; SEDLÁŘ, K.; WECKWERTH, W. OMICs prediction for Hordeum vulgare using Random Forest methodology. The Biomania Student Scientific Meeting 2022, Book of abstract. 1st. Brno: Masaryk University Press, 2022. p. 52-52. ISBN: 978-80-280-0040-0.
Detail

SCHWARZEROVÁ, J.; KOŠTOVAL, A.; BAJGER, A.; JAKUBIKOVA, L.; PIERDIES, I.; POPELINSKY, L.; SEDLÁŘ, K.; WECKWERTH, W. A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling. In Information Technology in Biomedicine. Springer, 2022. p. 498-509. ISBN: 978-3-031-09135-3.
Detail

SCHWARZEROVÁ, J.; PIERDIES, I.; SEDLÁŘ, K.; WECKWERTH, W. Linear Predictive Modeling for Immune Metabolites Related to Other Metabolites. In Bioinformatics and Biomedical Engineering. Springer, 2022. p. 16-27. ISBN: 978-3-031-07704-3.
Detail

NOVÁK, V. Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers. Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers. 1. Brno: Brno University of Technology, Faculty of Electrical Engineering and Communication, 2022. ISBN: 978-80-214-6029-4.
Detail

NEMČEKOVÁ, P.; SCHWARZEROVÁ, J. Dynamic metabolomic prediction based on genetic variation for Hordeum vulgare. In Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers. Brno: Brno University of Technology, Faculty of Electrical Engineering and Communication, 2022. p. 251-254. ISBN: 978-80-214-6029-4.
Detail

NEJEZCHLEBOVÁ, J.; SCHWARZEROVÁ, J. Operon identifier: Identification of operon structures in the whole genome. In Proceedings II of the 28th Conference STUDENT EEICT 2022 Selected Papers. Brno: Brno University of Technology, Faculty of electrical engineering and communication, 2022. p. 80-83. ISBN: 978-80-214-6030-0.
Detail

SCHWARZEROVÁ, J.; WECKWERTH, W.; WALTHER, D. Insight in single nucleotide polymorphisms focused on post transcriptional modifications in Arabidopsis thaliana. NGSymposium in Computational Biology. Warsaw: NGSymposium, 2022. p. 12-12.
Detail

SIDAK, D.; SCHWARZEROVÁ, J.; WECKWERTH, W.; WALDHERR, S. Interpretable machine learning methods for predictions in systems biology from omics data. Frontiers in Molecular Biosciences, 2022, vol. 9, no. October 2022, p. 1-28. ISSN: 2296-889X.
Detail

SCHWARZEROVÁ, J.; ZEMAN, M.; RYCHLÍK, I.; WECKWERTH, W.; PROVAZNÍK, I.; DOLEJSKÁ, M.; ČEJKOVÁ, D. Systems biology approach for analysis of mobile genetic elements in chicken gut microbiome. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE Computer Society, 2022. p. 2865-2870. ISBN: 978-1-4577-1799-4.
Detail

KOŠTOVAL, A.; SCHWARZEROVÁ, J. Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis. In Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers. 1. Brno: Brno University of Technology, Faculty of Electronic Engineering and Communication, 2022. p. 128-131. ISBN: 978-80-214-6029-4.
Detail

Odpovědnost: Schwarzerová Jana, Ing. et Ing., MSc