Large-scale collaborative precision medicine initiatives (e.g., The Cancer Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the resulting multi-omics data offer the tantalizing possibilities of realizing the potential of precision medicine in cancer prevention, diagnosis, and treatment by substantially improving our understanding of underlying mechanisms as well as the discovery of novel biomarkers for different types of cancers. However, such analyses present a number of challenges, including the heterogeneity of data types, and the high-dimensionality of omics data.
This project addresses the challenge of integrating heterogeneous high-dimensional data (e.g., multi-omics data) in order to develop testable hypothesis and reliable predictive models for deciphering phenotype-genotype relationships in complex diseases such as cancer. Our methodology is based on multi-view learning, representation learning, and graph mining approaches.
El-Manzalawy Y, Hsieh T-Y, Shivakumar M, Kim D, Honavar V (2018) Min-Redundancy and MaxRelevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data. BMC Medical Genomics 11 (Suppl 3) :71
El-Manzalawy Y (2018) CCA based multi-view feature selection for multi-omics data integration. IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). pp. 1-8.
El-Manzalawy Y, Abbas M, Le T, Honavar V (2018) Multi-Omics Data vs. Model Fusion for Cancer Survival Prediction. Extended Abstract Accepted for presentation at CAMDA2018, The 26th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB).