Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease
Конференцијски прилог (Објављена верзија)
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© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
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Приказ свих података о документуАпстракт
Parkinson’s disease (PD) is the second most common neurodegenerative disease which is
characterized by neuronal loss of dopaminergic neurons (mDA) in the substantia nigra. The
underlying complexity of the disease and limited amount of patient material limits current
interventions to only symptomatic and no curative treatment despite intensive research.
We use patient-derived induced pluripotent stem cells to generate mDAs and investigate
disease mechanisms by multiomics characterization including single cell RNA-sequencing
and bulk proteomics and metabolomics. For this purpose, we developed an extended Non-
Negative TriMatrix Factorization approache that allows to integrate the heterogeneous
omics data with knowledge of molecular databases including protein-protein, genetic and
metabolic interactions as well as co-expression profiles. Our approach was able to identify
already PD-associated but also new druggable candidate genes of PD development.
Кључне речи:
Parkinson’s Disease / neurodegenerative diseaseИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 32-32Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Skupin, Alexander PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1967 AB - Parkinson’s disease (PD) is the second most common neurodegenerative disease which is characterized by neuronal loss of dopaminergic neurons (mDA) in the substantia nigra. The underlying complexity of the disease and limited amount of patient material limits current interventions to only symptomatic and no curative treatment despite intensive research. We use patient-derived induced pluripotent stem cells to generate mDAs and investigate disease mechanisms by multiomics characterization including single cell RNA-sequencing and bulk proteomics and metabolomics. For this purpose, we developed an extended Non- Negative TriMatrix Factorization approache that allows to integrate the heterogeneous omics data with knowledge of molecular databases including protein-protein, genetic and metabolic interactions as well as co-expression profiles. Our approach was able to identify already PD-associated but also new druggable candidate genes of PD development. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease EP - 32 SP - 32 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1967 ER -
@conference{ author = "Skupin, Alexander", year = "2023", abstract = "Parkinson’s disease (PD) is the second most common neurodegenerative disease which is characterized by neuronal loss of dopaminergic neurons (mDA) in the substantia nigra. The underlying complexity of the disease and limited amount of patient material limits current interventions to only symptomatic and no curative treatment despite intensive research. We use patient-derived induced pluripotent stem cells to generate mDAs and investigate disease mechanisms by multiomics characterization including single cell RNA-sequencing and bulk proteomics and metabolomics. For this purpose, we developed an extended Non- Negative TriMatrix Factorization approache that allows to integrate the heterogeneous omics data with knowledge of molecular databases including protein-protein, genetic and metabolic interactions as well as co-expression profiles. Our approach was able to identify already PD-associated but also new druggable candidate genes of PD development.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease", pages = "32-32", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1967" }
Skupin, A.. (2023). Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 32-32. https://hdl.handle.net/21.15107/rcub_imagine_1967
Skupin A. Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease. in 4th Belgrade Bioinformatics Conference. 2023;4:32-32. https://hdl.handle.net/21.15107/rcub_imagine_1967 .
Skupin, Alexander, "Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease" in 4th Belgrade Bioinformatics Conference, 4 (2023):32-32, https://hdl.handle.net/21.15107/rcub_imagine_1967 .