Artificial intelligence in life sciences: Accelerating the future

Artificial intelligence (AI) is transforming biological research by enabling the analysis of massive datasets, uncovering genetic patterns, and accelerating the development of new therapies. Its impact is increasingly evident across diverse fields, including virology, gene activation, precision medicine, drug development, and diagnostics—reshaping scientific efforts around the globe.

AI in virus research

One of the primary areas of study in which AI has gained attention is virus research. Because viruses constantly evolve, they pose ongoing threats to global health—which ultimately puts rapid virus research and vaccine development in high demand. A team led by evolutionary biologist Justin Meyer at the University of California-San Diego (2023), is leveraging AI in an effort to remain ahead of this demand.   

Bacteriophages—or the viruses that infect bacteria—offer potential as therapeutic agents against antibiotic-resistant infections. However, their rapid evolution makes them difficult to analyze. AI tools have helped Meyer’s team process large datasets to identify genes involved in phage infection as well as learn how phages evolve and diversify into new species. The team now seeks to further analyze this influx of AI-derived data and determine the most important interactions. This analysis can help them focus on the genetic relationships that drive infection and resistance. It ultimately aims to further our understanding of viral behavior and pave the way for enhanced vaccine development.  

AI in gene activation

AI has been used to make strides in understanding gene activation—or the process by which a gene is activated or deactivated. Studying gene activation in detail is fundamental to understanding genetic diseases and developing effective targeted therapies. AI is helping to enhance this field of research by analyzing DNA sequences and downstream activity.  

For instance, researchers at the University of California-San Diego (2020) have used AI to identify the downstream core promoter region (DPR)—a 19-base pair element that is now known to play a significant role in gene activation. By generating and analyzing 0.5 x 10^6 random DNA sequences, AI models were able to predict DPR activity with remarkable accuracy. This prompted the team to use the same formula to create a model that also identified TATA box sequences. Together, these breakthroughs in AI not only help us understand and control gene activation but lay the groundwork for innovations in personalized medicine.  

AI in precision medicine

Recent research using AI has pushed precision medicine further into the spotlight. Precision medicine—which tailors treatments based on an individual’s genetic, environmental, and lifestyle factors—depends heavily on decoding each patient’s unique genetic information. As such, the need to analyze large amounts of data and identify genetic patterns continues to grow.

Fortunately, AI tools have been developed to do exactly that. A team of researchers recently used AI platforms DeepInsight and DeepInsight-3D to convert complex omics data into image-like formats (Sharma et al. 2024). This allowed convolutional neural networks to detect hidden genetic patterns—a key step toward improving our ability to predict diseases and drug responses. For instance, the team made a prediction model of drug efficacy using DeepInsight-3D and predicted anti-cancer drug responses with 72% accuracy, which is 7% better than other deep learning-based methods. As such, AI-driven research strategies like these will continue to pave the way for enhanced precision medicine. 

Explore AI in life sciences

AI is helping to revolutionize life sciences research from virus analysis to genetic discovery to precision medicine. Its ability to speed up the process of extracting meaningful insights from complex data is driving innovation. However, the remarkable potential of AI tools must be used responsibly. Researchers should continually verify AI-generated findings to ensure the scientific rigor of their work. As AI applications in research continue to diversify, responsible use will be the key to groundbreaking—and reliable—biological insights. 

References

University of California-San Diego. Artificial intelligence drives new frontiers in biology. School of Biological Sciences (2023).

University of California-San Diego. Artificial intelligence aids gene activation discovery. ScienceDaily (2020).

Sharma A, et al. Advances in AI and machine learning for predictive medicine. J Hum. Genet., 69, 487–497 (2024).