Dissertation Defense of CMSE Julian Venegas
Department of Computational Mathematics, Science & Engineering
Michigan State University
Dissertation Defense Notice
Tuesday, July 30th, 2024 at 11:00 AM EST
ZOOM ONLY: https://msu.zoom.us/j/3547068677
Towards Integrative Learning of Cellular Systems and Networks
Julian Venegas
Abstract:
Advances in omics technologies have led to an abundance of comprehensive biomolecular information of biological systems, down to single-cell resolution. With omics data, biologists can gain a deeper understanding of the complex-hierarchical networks that constitute an organism. To this end, deep learning methods are often applied to assist in discovering meaningful patterns and relationships from omics data. Though deep learning methods can offer high performance on many complex tasks, some challenges arise with omics-based tasks: (1) Omics data are often high-dimensional with low-sample size and/or high levels of sparsity, with complex dependency structures between and within omics data types. (2) There is an imbalance of annotated data across different species and environments. These difficulties make desirable the integration of omics data across different modalities, group samples, and platforms, as well as environments and species.
This thesis examines, builds and implements approaches to address these challenges through Integrative Learning techniques, which I use as a general term to encompass techniques that incorporate multiple sources of related data for improved learning (e.g., transfer learning, multi-task learning, and multi-modal data integration). In this work I highlight and address these challenges in different omics-based tasks. Next, I expand on these ideas to integrate omics data with chemical compound data for in-silico molecular property prediction tasks. Here, I focus on essential tasks that facilitate the drug discovery process.
Lastly, I suggest potential strategies and directions to build on these ideas, particularly towards the interface of bioinformatics and cheminformatics, through the integration of omics and chemical compound data.
Committee Members:
Dr. Yuying Xie (chair)
Dr. Shin-Han Shiu
Dr. Frederi Viens
Dr. Longxiu Huang
