Development of an Artificial Intelligence Model for Genomic Analysis Based on Multidimensional Data
Keywords:
Artificial intelligence, Genomic analysis, Multidimensional data, Multi-omics, Socio-technical systemAbstract
This study aims to analyze the development of an artificial intelligence model for genomic analysis based on multidimensional data, with attention to the technical, interpretive, ethical, and institutional issues involved in genomic AI development. The study addresses the growing complexity of genomic research, where genomic, transcriptomic, epigenomic, proteomic, metabolomic, and clinical data must be integrated to support more accurate and meaningful analysis. A qualitative approach with an exploratory case study design was used. Data were collected through semi-structured interviews, limited participatory observation, and documentation involving 18 to 25 participants, including bioinformaticians, data scientists, genomic researchers, molecular biologists, clinical researchers, laboratory managers, and data governance actors. The data were analyzed using thematic analysis. The findings reveal five main themes: data heterogeneity as the main analytical challenge, interdisciplinary translation as a condition for model relevance, interpretability as a basis for trust, validation as a shared technical and institutional process, and ethical governance as a foundation for responsible genomic AI development. These findings show that genomic AI is not only a computational tool but also a socio-technical system shaped by data quality, expert collaboration, institutional capacity, and ethical responsibility. The study contributes to theoretical discussions on AI in genomics and offers practical guidance for developing transparent, valid, and accountable AI models. Future research should combine qualitative inquiry with technical model evaluation and explore public perspectives on genomic data use.
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