DISSERTATION DEFENSE

Department of Computer Science and Engineering

University of South Carolina

Author : Fengyao Yan

Advisor : Dr. Yan Tong, Dr. Jijun Tang

Date : Oct 24th, 2024

Time:  9:30 am

Place : Zoom Meeting

Link: https://us05web.zoom.us/j/82857019481?pwd=ueKsnxBVTLySbXb4yj4Z93pAzb7va…

Meeting ID: 828 5701 9481
Passcode: 747882

One tap mobile
+12532158782,,82857019481#,,,,*747882# US (Tacoma)
+13462487799,,82857019481#,,,,*747882# US (Houston)

Dial by your location
• +1 253 215 8782 US (Tacoma)
• +1 346 248 7799 US (Houston)
• +1 669 444 9171 US
• +1 669 900 9128 US (San Jose)
• +1 719 359 4580 US
• +1 253 205 0468 US
• +1 507 473 4847 US
• +1 564 217 2000 US
• +1 646 558 8656 US (New York)
• +1 646 931 3860 US
• +1 689 278 1000 US
• +1 301 715 8592 US (Washington DC)
• +1 305 224 1968 US
• +1 309 205 3325 US
• +1 312 626 6799 US (Chicago)
• +1 360 209 5623 US
• +1 386 347 5053 US

Meeting ID: 828 5701 9481
Passcode: 747882

Find your local number: https://us05web.zoom.us/u/leCEnSLjPT

Abstract

      Genes can be treated as a graph that can be mapped. Tremendous information is coded in genes to ensure a complex functioning organism. Decoding this information is critical to understanding our biology and developing treatments for various diseases including cancer. Deep learning, a new branch of computer science, has gained traction over the past decade. It offers more insight into the data that is processed by the deep-learning models. Our study has shown that deep-learning models can be an effective tool in decoding genetic data such as gene tissue-deconvolution, gene graph mapping and genomic imputation. In tasks such as tissue deconvolution, our research has demonstrated the superior capability of deep learning-based approaches in capturing sample variations compared to traditional numerical analytical methods. While our approach requires large relevant datasets for effective deep learning training, this challenge can be addressed with increasing data availability. In gene graphing and mapping, our Graph Neural Network based approach consistently outperforms traditional regression techniques by a significant margin. The primary challenge here lies in the demand for substantial computing resources; however, the ongoing growth in average computing power and the enhanced accessibility of computational resources are expected to alleviate this constraint over time. Moreover, in the realm of generating and imputing missing biological data, cutting-edge generative AI models have proven to be invaluable. We are actively exploring the potential of generative AI to aid in imputing common missing biological data such as gene expression or methylation states. Overall, the evolution of advanced deep learning models has introduced fresh perspectives and possibilities to the field of biology and medicine, albeit accompanied by certain challenges. By addressing these challenges, deep learning models exhibit remarkable efficacy in resolving complex biomedical issues. In the foreseeable future, these advancements hold the promise of unveiling novel biological insights and facilitating the development of innovative treatments, thereby propelling biomedical research forward and ultimately benefiting humanity.