Large Language Models in Genomics—A Perspective on Personalized Medicine DOI Creative Commons
Shahid Ali, Yazdan Ahmad Qadri, Khurshid Ahmad

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(5), P. 440 - 440

Published: April 23, 2025

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing field of medicine. LLMs possess capability to analyze scientific literature and genomic data by comprehending producing human-like text. This enhances accuracy, precision, efficiency extensive analyses through contextualization. have made significant advancements in their ability understand complex genetic terminology accurately predict medical outcomes. These capabilities allow for a more thorough understanding influences on health issues creation effective therapies. review emphasizes LLMs’ impact healthcare, evaluates triumphs limitations processing, makes recommendations addressing these order enhance system. It explores latest analysis, focusing enhancing disease diagnosis treatment accuracy taking account an individual’s composition. also anticipates future which AI-driven analysis commonplace clinical practice, suggesting potential research areas. To effectively leverage personalized medicine, it vital actively support innovation across multiple sectors, ensuring that AI developments directly contribute solutions tailored individual patients.

Language: Английский

Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities DOI
Chong Chen,

K Zhao,

Jiewu Leng

et al.

Robotics and Computer-Integrated Manufacturing, Journal Year: 2025, Volume and Issue: 94, P. 102982 - 102982

Published: Feb. 10, 2025

Language: Английский

Citations

1
FusionESP: Improved Enzyme–Substrate Pair Prediction by Fusing Protein and Chemical Knowledge DOI
Zhenjiao Du, Weimin Fu,

Xiaolong Guo

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: March 4, 2025

To reduce the cost of experimental characterization potential substrates for enzymes, machine learning prediction models offer an alternative solution. Pretrained language models, as powerful approaches protein and molecule representation, have been employed in development enzyme-substrate achieving promising performance. In addition to continuing improvements effectively fusing encoders handle multimodal tasks is critical further enhancing model performance by using available representation methods. Here, we present FusionESP, a architecture that integrates chemistry with two independent projection heads contrastive strategy predicting pairs. Our best achieved state-of-the-art accuracy 94.77% on test data exhibited better generalization capacity while requiring fewer computational resources training data, compared previous studies fine-tuned encoder or employing more encoders. It also confirmed our hypothesis embeddings positive pairs are closer each other high-dimension space, negative exhibit opposite trend. ablation showed played crucial role enhancement, improved heads' classification tasks. The proposed expected be applied enhance additional multimodality biology. A user-friendly web server FusionESP established freely accessible at https://rqkjkgpsyu.us-east-1.awsapprunner.com/.

Language: Английский

Citations

1
Evaluations of Large Language Models in Computational Fluid Dynamics: Leveraging, Learning and Creating Knowledge DOI Creative Commons

L. Wang,

Lei Zhang, Guowei He

et al.

Theoretical and Applied Mechanics Letters, Journal Year: 2025, Volume and Issue: unknown, P. 100597 - 100597

Published: April 1, 2025

Language: Английский

Citations

0
Large Language Models in Genomics—A Perspective on Personalized Medicine DOI Creative Commons
Shahid Ali, Yazdan Ahmad Qadri, Khurshid Ahmad

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(5), P. 440 - 440

Published: April 23, 2025

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing field of medicine. LLMs possess capability to analyze scientific literature and genomic data by comprehending producing human-like text. This enhances accuracy, precision, efficiency extensive analyses through contextualization. have made significant advancements in their ability understand complex genetic terminology accurately predict medical outcomes. These capabilities allow for a more thorough understanding influences on health issues creation effective therapies. review emphasizes LLMs’ impact healthcare, evaluates triumphs limitations processing, makes recommendations addressing these order enhance system. It explores latest analysis, focusing enhancing disease diagnosis treatment accuracy taking account an individual’s composition. also anticipates future which AI-driven analysis commonplace clinical practice, suggesting potential research areas. To effectively leverage personalized medicine, it vital actively support innovation across multiple sectors, ensuring that AI developments directly contribute solutions tailored individual patients.

Language: Английский

Citations

0