2026-09-24T12:48:47Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/121527 2023-12-13 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Peng, Jian Peng, Jian Han, Jiawei Zhai, Chengxiang Liu, Qiang 2023-08 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms The student, Yang Liu, accepted the attached license on 2023-07-13 at 19:16. The student, Yang Liu, submitted this Dissertation for approval on 2023-07-13 at 19:32. This Dissertation was approved for publication on 2023-07-14 at 10:58. DSpace SAF Submission Ingestion Package generated from Vireo submission #19687 on 2023-12-04 at 17:02:36 Modeling protein sequences, structures and functions with deep neural networks Liu, Yang 2023-07-14 Machine Learning Deep Learning Artificial Intelligence Computational Biology Bioinformatics In the rapid-advancing field of biotechnology, proteins - the fundamental building blocks of life - play a critical role in addressing an array of complex biological challenges. As the cost of experimentation is extremely high and various biological datasets have been created, computational methods for understanding proteins have become essential. In this dissertation, we introduced several machine learning algorithms aiming at improving protein structure and function modeling by leveraging data-driven principles. First, we introduce a deep learning approach for protein contact prediction which uses a deep convolutional network to learn meaningful structural motifs based on experimental data. Second, we detail a data-driven method for learning protein structural representation enabling both high-performance and high-efficiency structural searches. Third, we introduce an end-to-end protein network alignment learning algorithm that integrates heterogeneous information from biological network using graph neural networks. In summary, these developments have demonstrated the potential of applying data-driven principle through novel machine learning algorithms to address the challenges in protein modeling, yielding learned biological insights. Text eng https://hdl.handle.net/2142/121527 Copyright 2023 Yang Liu Ph.D. Dissertation Computer Science University of Illinois at Urbana-Champaign Computer Science