Machine Learning in Multi-Omics (MLiMO)

Institution: Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora (IHSM)

Members (researchers): Ian Morilla

Home page: https://mlimowebsite.faculty.bio/
Contact email: ian.morilla@ihsm.uma-csic.es

Tools: GeoTop, TaelCore, CoMM-BIP,  S<sup>2</sup>-PepAnalyst

There are no service units associated with this research group.

At MLiMO, we harness advanced machine learning and integrative multi-omics approaches to unravel the complexity of biological systems. Our interdisciplinary research spans from fundamental questions in tumour micro-environments to dynamic modelling in plant and human diseases. Through robust data-driven methodologies, we uncover the principles that orchestrate life’s molecular networks—translating insights into advances for precision medicine, sustainable agriculture, and beyond.

Research topics:
Publications

Gouiaa, F., Vomo-Donfack, K. L., Tran-Dinh, A., & Morilla, I. (2024). Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction. Computers in Biology and Medicine, 169(107969), 107969. doi:10.1016/j.compbiomed.2024.107969

Gauthier, S., Tran-Dinh, A., & Morilla, I. (2023). Plasma proteome dynamics of COVID-19 severity learnt by a graph convolutional network of multi-scale topology. Life Science Alliance, 6(5), e202201624. doi:10.26508/lsa.202201624

Abaach, M., & Morilla, I. (2023). GeoTop: Advancing image classification with geometric-topological analysis. Retrieved from http://arxiv.org/abs/2311.16157

Tran-Dinh, A., Laurent, Q., Even, G., Tanaka, S., Lortat-Jacob, B., Castier, Y., … Morilla, I. (2022). Personalized risk predictor for acute cellular rejection in lung transplant using soluble CD31. Scientific Reports, 12(1), 17628. doi:10.1038/s41598-022-21070-1

Benadjaoud, M. A., Soysouvanh, F., Tarlet, G., Paget, V., Buard, V., Santos de Andrade, H., … Milliat, F. (2022). Deciphering the dynamic molecular program of radiation-induced endothelial senescence. International Journal of Radiation Oncology, Biology, Physics, 112(4), 975–985. doi:10.1016/j.ijrobp.2021.11.019

Morilla, I., Chan, P., Caffin, F., Svilar, L., Selbonne, S., Ladaigue, S., … Guipaud, O. (2022). Deep models of integrated multiscale molecular data decipher the endothelial cell response to ionizing radiation. iScience, 25(1), 103685. doi:10.1016/j.isci.2021.103685

Morilla, I., Uzzan, M., Cazals-Hatem, D., Colnot, N., Panis, Y., Nancey, S., … Daniel, F. (2021). Computational learning of microRNA-based prediction of pouchitis outcome after restorative proctocolectomy in patients with ulcerative colitis. Inflammatory Bowel Diseases, 27(10), 1653–1660. doi:10.1093/ibd/izab030

Morilla, I. (2020). A deep learning approach to evaluate intestinal fibrosis in magnetic resonance imaging models. Neural Computing & Applications, 32(18), 14865–14874. doi:10.1007/s00521-020-04838-2

Morilla, I., Uzzan, M., Laharie, D., Cazals-Hatem, D., Denost, Q., Daniel, F., … Treton, X. (2019). Colonic MicroRNA profiles, identified by a deep learning algorithm, that predict responses to therapy of patients with acute severe ulcerative colitis. Clinical Gastroenterology and Hepatology: The Official Clinical Practice Journal of the American Gastroenterological Association, 17(5), 905–913. doi:10.1016/j.cgh.2018.08.068

Sharma, A., Heinze, S. D., Wu, Y., Kohlbrenner, T., Morilla, I., Brunner, C., … Bopp, D. (2017). Male sex in houseflies is determined by Mdmd , a paralog of the generic splice factor gene CWC22. Science (New York, N.Y.), 356(6338), 642–645. doi:10.1126/science.aam5498

Research lines
Funding
More info

Patents: Methods for predicting acute severe colitis treatment response (Medical Kits), 17340534-Issued.
Methods for predicting acute severe colitis treatment response (DeepCol Software), US11060147B2-Issued.