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Call for Papers: Artificial Intelligence and Machine Learning for Environmental & Health

Artificial intelligence (AI) and machine learning (ML) are transformative fields of computer science. They empower researchers to develop algorithms and models capable of extracting meaningful insights, making predictions, and automating tasks by analyzing and learning from data. Due to the intricate nature of environmental health issues, the integration of AI and machine learning is imperative. These advanced computational technologies have the potential to revolutionize environmental health studies through fundamentally improve and advance environmental exposure assessment, environmental health risk assessment, and environmental policy development.

This Virtual Special Issue from Environment & Health extends an invitation to scientists to share their innovative work on leveraging AI and ML for environmental health studies.

We welcome contributions of Articles, Reviews, Perspectives or Viewpoints that delve into topics including, but not limited to:

Source appointment Chemical toxicity prediction Identification or screening of unknown pollutants Human exposure assessment Molecular mechanisms between exposure and disease Data compliance and ethics By sharing these findings or perspectives, we hope to spur further innovation and advancements in this critical and rapidly evolving field.

Organizing Editors Miao Yu, Ph.D., Guest Editor The Jackson Laboratory, USA

Mingliang Fang, Ph.D., Guest Editor Fudan University, China

Zhenyu Tian, Ph.D., Guest Editor Northeastern University, USA

Bin Wang, Ph.D., Guest Editor Peking University, China

Douglas Walker, Ph.D., Guest Editor Emory University, USA

Yuming Guo, Ph.D., Associate Editor, Environment & Health Monash University, Australia