Artificial Intelligence Strategies in Biological Sciences: Transforming Research, Analysis, and Discovery
Article Sidebar
Main Article Content
Abstract: "Artificial Intelligence (AI) has emerged as a transformative instrument in biological sciences, facilitating the analysis of complex biological datasets, predicting molecular interactions, and automating laboratory and field operations.". This paper explores key AI strategies—machine learning, deep learning, computer vision, and natural language processing (NLP)—and their applications across genomics, proteomics, taxonomy, ecology, and medical diagnostics. Recent advancements in artificial intelligence include the utilization of models such as convolutional neural networks (CNNs) and support vector machines (SVMs). The integration of AI with biological databases accelerates drug discovery, species classification, and environmental assessment. The study highlights future directions and ethical considerations in implementing AI responsibly for sustainable biological research.
Downloads
References
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015). Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnology, 33(8), 831–838. https://doi.org/10.1038/nbt.3300
Chen, Y., Xie, J., & Wang, Y. (2023). Artificial intelligence applications in biological data interpretation: A comprehensive review. Frontiers in Artificial Intelligence, 6, 115234. https://doi.org/10.3389/frai.2023.115234
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Jiang, D., Armour, C. R., Hu, C., & Zhang, J. (2022). Machine learning in biology and medicine: An overview. Bioinformatics Advances, 2(1), vbac009. https://doi.org/10.1093/bioadv/vbac009
Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., & Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321–332. https://doi.org/10.1038/nrg3920
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences, 115(25), E5716–E5725. https://doi.org/10.1073/pnas.1719367115
Singh, A., Kumar, P., & Sharma, R. (2024). AI-driven approaches for sustainable biodiversity monitoring and ecosystem modeling. Environmental Monitoring and Assessment, 196(4), 215. https://doi.org/10.1007/s10661-024-11623-5
Tsubaki, M., Tomii, K., & Sese, J. (2019). Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics, 35(2), 309–318. https://doi.org/10.1093/bioinformatics/bty535
H., Allot, A., & Lu, Z. (2020). PubTator Central: Automated concept annotation for biomedical full-text articles. Nucleic Acids Research, 48(W1), W562–W570. https://doi.org/10.1093/nar/gkaa397
Yildiz, H., & Yüksel, A. Y. (2025). Integrating AI in biological sciences: Emerging opportunities and challenges in bioinformatics, drug design, and ecology. Computational Biology and Chemistry, 110, 108743. https://doi.org/10.1016/j.compbiolchem.2025.108743
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.