TRANSFORMASI PENELITIAN GENOMIK MELALUI INTEGRASI BIG DATA, AI, DAN ANALISIS MULTI-OMICS

Abstrak

The advancement of genome sequencing technology has yielded vast and complex volumes of biological data. Traditional analytical methods are often inadequate for efficient data management and interpretation. Consequently, integrating big data, Artificial Intelligence (AI), and multiomics analysis presents a viable solution to these challenges, serving as a critical driver for progress in modern biological research. This study examines the role of Big Data and AI integration in accelerating genomic research processes, identifying implementation challenges and opportunities across infrastructure, computational analysis, and biological data ethics. A literature review was performed on various national and international publications regarding the application of Big Data, AI, and multiomics in genomics. The findings suggest that Big Data and AI streamline the analysis of large scale genomic data, such as Next Generation Sequencing (NGS), via real time processing platforms. Additionally, AI through machine learning and deep learning identifies intricate patterns in multiomics datasets to predict genetic variants. Primary challenges include infrastructure limitations, high computational demands, privacy issues, and algorithmic bias, though these concurrently foster innovations in bioinformatics and personalized medicine. In conclusion, the integration of Big Data and AI is fundamental to accelerating genomic research and facilitating more precise, ethical, secure, and collaborative analytical frameworks.