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4268. Genomic Map of Poland versus artificial intelligence - opportunities, challenges and threats
Invited abstract in session TD-20: Advancements in AI and Genomics: Bridging Technology and Biology for Future Healthcare Solutions, stream Computational Biology, Bioinformatics and Medicine.
Tuesday, 14:30-16:00Room: 45 (building: 116)
Authors (first author is the speaker)
| 1. | Piotr Lukasiak
|
| Institute of Computing Science, Poznan University of Technology | |
| 2. | Aleksandra Swiercz
|
| Institute of Computing Science, Poznan University of Technology | |
| 3. | Maciej Majchrzak
|
| Poznan University of Technology | |
| 4. | Jacek Blazewicz
|
| Institute of Computing Science, Poznan University of Technology |
Abstract
Computing, together with computational tools supported by machine learning, plays a key role in the creation of national genomic maps by enabling the analysis, interpretation and use of vast amounts of genetic information from multiple sources. Operations research methods are essential for understanding the genetic diversity of populations and for advances in personalised medicine and disease inheritance research. They influence the modelling processes of genome sequencing, comparison with reference genomes, identification of genetic variants and assessment of their functional consequences. Data processing enables the integration of genetic information from different sources, enabling the understanding of the relationships between genetic variation and phenotypic traits and diseases, as well as the identification of population structure and the analysis of genetic relationships between individuals in a population. The Genomic Map of Poland is a set of unique databases of genomic sequences reflecting the genetic picture of the Polish population, together with computational and visualisation tools that allow the analysis and interpretation of large amounts of genetic data and the integration of different sources of information, leading to an understanding of the genetic diversity of the population and the identification of relationships between genes and phenotypic traits and diseases.
Keywords
- Computational Biology, Bioinformatics and Medicine
- Analytics and Data Science
- Computer Science/Applications
Status: accepted
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