Li, P.; Parvej, M.M.; Zhang, C.; Guo, S.; Zhang, J. Advances in the Development of Representation Learning and Its Innovations against COVID-19. COVID2023, 3, 1389-1415.
Li, P.; Parvej, M.M.; Zhang, C.; Guo, S.; Zhang, J. Advances in the Development of Representation Learning and Its Innovations against COVID-19. COVID 2023, 3, 1389-1415.
Li, P.; Parvej, M.M.; Zhang, C.; Guo, S.; Zhang, J. Advances in the Development of Representation Learning and Its Innovations against COVID-19. COVID2023, 3, 1389-1415.
Li, P.; Parvej, M.M.; Zhang, C.; Guo, S.; Zhang, J. Advances in the Development of Representation Learning and Its Innovations against COVID-19. COVID 2023, 3, 1389-1415.
Abstract
In bioinformatics research, traditional machine learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompasses non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-dimensional, sparse data, detects complex biological patterns, and optimizes predictive performance. In recent years, graph representation learning has become a popular research direction. It embeds graphs into a low-dimensional space while preserving the structural and attribute information of the graph, enabling better feature extraction for downstream tasks. This study extensively reviews representation learning advancements, particularly in the research of representation methods since the emergence of COVID-19. We begin with an analysis and classification of neural network-based language model representation learning techniques as well as graph representation learning methods. Subsequently, we explore their methodological innovations in the context of COVID-19, with a focus on the domains of drugs, public health, and healthcare. Furthermore, we discuss the challenges and opportunities associated with graph representation learning. This comprehensive review presents invaluable insights for researchers as it documents the development of COVID-19 and offers experiential lessons to preempt future infectious diseases. Moreover, this study guides future bioinformatics and biomedicine research methodologies.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
