Data-Driven Drug Repurposing for COVID-19

The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June, 2021, according to the World Health Organization. Since the initial reported from December 2019 in Wuhan, China, COVID-19 has demonstrated a high transmission rate (with a R0 > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and tremendous burden on health care systems around the world. To understand the serious and complex disease and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex disease. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

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Advances in the Development of Representation Learning and Its Innovations against COVID-19

Li,

Parvej,

Zhang

et al. 2023

COVID

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In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass 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 and sparse data, detects complex biological patterns, and optimizes predictive performance. In recent years, graph representation learning has become a popular research topic. It involves the embedding of 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 provides guidance regarding future bioinformatics and biomedicine research methodologies.

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Data-Driven Drug Repurposing for COVID-19

Cited by 5 publications

References 48 publications

Advances in Network-Based Drug Repositioning

Advances in Network-Based Drug Repositioning

Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

Advances in the Development of Representation Learning and Its Innovations against COVID-19

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