Researchers at the University of Missouri have updated their AI model, MULocDeep, to accurately predict protein location within cells of animals, humans, and plants, aiming to advance disease treatment. Abnormal protein activity associated with conditions like metabolic disorders and cancer can be identified using the model. It also has the potential for drug development. The researchers are developing an online course, and previous studies show AI’s potential for enhancing disease diagnosis, such as predicting cardiovascular diseases.
Researchers at the University of Missouri have made significant updates to their AI model, MULocDeep, to improve its ability to predict the location of proteins within the cells of animals, humans, and plants. The primary objective of this development is to advance the treatment of diseases by gaining a better understanding of protein activity within cells.
Identifying the precise location of proteins within a cell is highly valuable as it provides crucial biological information related to diseases, given that proteins are involved in numerous cellular activities. The original AI model was created by Dong Xu, a Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science, and Jay Thelen, a professor of biochemistry, a decade ago. It was initially designed to analyze proteins in mitochondria. Now, the model has been enhanced to make accurate predictions of protein localization applicable across various organisms.
The researchers believe that leveraging AI to enhance predictions will assist in identifying abnormal protein activity, which is often associated with metabolic disorders, cancer, and neurological disorders. The mislocalization of proteins can lead to the inability of proteins to function as expected, either due to their inability to reach the target location or their inefficient movement within the cell.
In addition to facilitating the understanding of cellular functions, this AI tool has the potential to support drug development. The objective of pharmaceutical development in this context would be to ensure the proteins are transported to the correct cellular location when necessary.
While the researchers acknowledge that they do not provide direct solutions for drug development or disease treatments, they believe their tool can be of great assistance to other scientists working in these areas. They emphasize the collaborative nature of scientific progress and how different contributions can collectively lead to significant advancements in the field.
Furthermore, the team led by Dong Xu is developing an online course that will educate students about the biological concepts underlying the AI model. This educational initiative aims to foster a better understanding of the model’s principles and promote further research in the field.
Recent studies have demonstrated the success of utilizing AI and machine learning in analyzing biological information to enhance disease diagnosis. For example, researchers at Rutgers, the State University of New Jersey, utilized AI and ML to examine genes in DNA and predict cardiovascular diseases. Through their analysis, they discovered correlations between specific genes and cardiovascular disease, while also identifying the influence of factors like age, gender, and race on patient outcomes.
The ongoing advancements in AI models and their applications in the medical field hold tremendous potential for improving disease treatment, diagnosis, and overall patient care.
