In this study, published in the journal Computers in Biology and Medicineresearchers have developed Artificial Intelligence (AI) that represents an important advance in the combination of deep learning and computational biology.
A team of researchers from the University of Granada and the University of Seville, led by Juan Antonio Marchal Corrales and Miguel Ángel Gutiérrez Naranjo respectively, has published an innovative study in which Artificial Intelligence is designed to improve the prediction of the evolution of cell migration in breast cancer. The study, titled Using Deep Learning for Predicting the Dynamic Evolution of Breast Cancer Migrationsuppose an important advance in the combination of techniques of deep learning and computational biology.
The multidisciplinary work, with the participation of Francisco M. García Moreno and the doctoral student Jesús Ruiz Espigares, both from the University of Granada, focuses on the development of a predictive framework called Prediction Wound Progression Framework (PWPF). This framework harnesses the power of deep learning to analyze and predict cell migration in two-dimensional models —technically known by Wound Healing—, providing new perspectives in understanding the metastatic process of breast cancer.
“Metastasis is the main cause of mortality in breast cancer patients and understanding how cell migration occurs is crucial to develop better therapeutic strategies.“explains Jesús Ruiz, co-principal investigator of the Department of Human Anatomy and Embryology of the University of Granada and member of the Biomedical Research Center (CIBM).
The team has developed a neural network architecture based on Conv-LSTM, which takes advantage of both spatial and temporal characteristics of cell migration data. This architecture allows you to accurately predict the evolution of the Wound Healing technique over time, improving the ability to analyze dynamics in the context of breast cancer models. This automated approach can be applied to more complex 3D models that better mimic the characteristics of tumors and promises to open new avenues for cancer research and treatment.
The research is the result of a multidisciplinary collaboration between different departments and centers: the Department of Computer Languages and Systems (LSI), the Department of Human Anatomy and Embryology and the CITIC of the University of Granada, the Singular BioFabi3D_Biofabrication and 3D (bio)printing Laboratory of the CIBM, the “Modeling Nature” Unit of Excellence and the ibs.GRANADA Biosanitary Research Institute, as well as the Department of Computer Sciences and Artificial Intelligence of the University of Seville.
The team’s progress not only stands out for its scientific contribution, but also for its accessibility and promotion of open accessas the code and data generated are publicly available in its GitHub and Zenodo repositories, encouraging open access and international collaboration in cancer research.
The project has been carried out thanks to funding from the Ministry of Science, Innovation and Universities (MICIN), the Department of Family Health of the Government of Andalusia and the Doctores Galera and Requena Chair of Research in Cancer Stem Cells of the UGR.
Bibliographic reference:
Garcia-Moreno FM, Ruiz-Espigares J, Gutiérrez-Naranjo MA, Marchal JA. Using deep learning for predicting the dynamic evolution of breast cancer migration. Comput Biol Med. 2024 Sep;180:108890. doi: 10.1016/j.compbiomed.2024.108890. Epub 2024 Jul 27. PMID: 39068903.
