Repositories storing biological sequences such as DNA, RNA, and protein sequences, facilitating research and analysis in bioinformatics.
Published in Chapter:
AI in Bioinformatics and Computational Biology
Copyright: © 2024
|Pages: 13
DOI: 10.4018/979-8-3693-3629-8.ch014
Abstract
The integration of artificial intelligence (AI) techniques with bioinformatics and computational biology has enabled unmatched insights into complex biological systems and processes. This has paved the way for groundbreaking innovations in biomedicine and biotechnology, with the potential to revolutionize drug discovery, personalized medicine, and therapeutic strategies. AI algorithms, including machine learning, deep learning, natural language processing, and data mining, have proven to be powerful tools for analyzing large biological datasets and extracting meaningful insights. Collaborations between computer scientists, biologists, and clinicians are essential in harnessing the potential of AI in biology and medicine. Ongoing research and interdisciplinary collaboration are crucial to address ethical challenges such as data privacy, patent laws, and the bioethics of AI algorithms. Future advancements in AI algorithms tailored for bioinformatics applications hold immense promise in enhancing data quality and interpretability and driving transformative innovations in healthcare.