Over the last years, challenges seen in different application areas in biology have driven the development of novel inference approaches that have contributed to better understanding of human biology. This special issue welcomes articles that focus on providing solutions to challenging biomedical problems occurring in fields such as genetic epidemiology, infectious diseases, oncology, precision medicine, and cell dynamics, amongst others. It focuses on approaching bioinference problems with novel, reproducible solutions which sit at the interface of statistics, mathematics, and computer science including machine learning. All submitted papers should be accompanied with open-sourced codes which are designed and documented for verifiable, reusable, and extensible research.
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Guest editors:
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Marina Evangelou - Imperial College London, London, United Kingdom
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Enrico Bibbona - Department of Mathematical Sciences, Polytechnic of Turin, Turin, Italy
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Ioana Bouros - University of Oxford, Oxfordshire, United Kingdom
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Aden Forrow - The University of Maine, Maine, USA
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Julia Brettschneider - University of Warwick, Warwick, United Kingdom
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Fergus Cooper - University of Oxford, Oxfordshire, United Kingdom
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Richard Creswell - University of Oxford, Oxfordshire, United Kingdom
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Yongchao Huang - University of Aberdeen, Aberdeen, Scotland
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Ben Lambert - University of Oxford, Oxfordshire, United Kingdom