Computational Methods for Multi-dimensional Data Integration to Improve Phenotype Prediction

Sponsor: National Science Foundation

PI: Wei Zhang

Multi-omics is the integration and analysis of multiple types of biological data, including genomics, transcriptomics, proteomics, and epigenomics. By combining these diverse omics data, researchers can gain a comprehensive understanding of biological systems at various molecular levels. However, the integration of data from different omics platforms is challenging due to the varying characteristics and quality of the generated data. Another obstacle is deciphering the complex interactions and regulatory networks across different omics layers, along with understanding their temporal dynamics. Additionally, the interpretability of multi-omics models and the translation of their findings into actionable biological insights remain ongoing challenges for successful phenotype prediction using multi-omics approaches. To tackle these research challenges, Dr. Wei Zhang, a member of the Genomics and Bioinformatics Cluster, aims to develop a machine learning-based multi-dimensional multi-omics data integration system in this project. This system will extract more accurate molecular signatures for biological interpretation and phenotype prediction. The project’s outcomes will reduce barriers in analyzing high-dimensional omics profiles and minimize the time and costs typically associated with biological and biomedical research.