Challenge-Enabled Machine Learning to Drug-Response Prediction
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Mini-Review Theme: Applications of Machine Learning and AI to Drug Discovery, Development, and Regulations Guest Editors: Lawrence Yu, Hao Zhu and Qi Liu
Challenge-Enabled Machine Learning to Drug-Response Prediction Ziyan Wang,1 Hongyang Li,2 Christopher Carpenter,3 and Yuanfang Guan2,3,4
Received 1 June 2020; accepted 29 July 2020 Abstract. In recent decades, the advancement of computational algorithms and the availability of big data have enabled artificial intelligence (AI) to dramatically improve predictive performance in nearly all research areas. Specifically, machine learning (ML) techniques, a major branch of AI, have been widely used in many tasks of drug discovery and development, including predicting treatment effects, identifying target genes and functional pathways, as well as selecting potential biomarkers. However, in practice, blindly applying ML methods may lead to common pitfalls, including overfitting and lack of generalizability. Therefore, how to improve the robustness and prediction accuracy of ML methods has become a crucial problem for researchers. In this review, we summarize the application of ML models to drug discovery by introducing the top-performing methods developed from large-scale drug-related data challenges in recent years. KEY WORDS: artificial intelligence; data challenge; drug discovery; machine learning.
INTRODUCTION/BACKGROUND The term artificial intelligence first came about in 1956 (1), and since then, humans have been enamored by the idea of computers that can learn on their own, as shown in numerous movies and novels. Although these works frequently depict AI systems that rival or even surpass human intelligence, the reality is that current AI systems possess less than 1% of the computational power of the human brain (2). Despite their inferiority to a human brain, today’s AI systems are extraordinarily useful and with the advent of large datasets, as well as advances in algorithms and hardware, AI has become an essential tool for engineers, researchers, and scientists to help improve the world. Machine learning (ML) techniques are an application of AI which allows a machine to use prior knowledge and data in order to learn and improve upon a known algorithm (3–4). The process of machine learning starts with giving the computer a set of data and a set of algorithms and letting the machine use these algorithms to find a pattern in the data Ziyan Wang and Hongyang Li contributed equally to this work. Guest Editors: Lawrence Yu, Hao Zhu and Qi Liu 1
Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA. 2 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA. 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA. 4 To whom correspondence should be addressed. (e–mail: [email protected])
(5). The goal is that after this process, the machine will be able to apply the techniques it learned to a previously unseen dataset. The unique feature of M
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