Journal
MOLECULAR ONCOLOGY
Volume 15, Issue 4, Pages 817-829Publisher
WILEY
DOI: 10.1002/1878-0261.12920
Keywords
artificial intelligence; cancer research; data granularity; machine learning
Categories
Funding
- European Union's Horizon 2020 research and innovation program [826121]
Ask authors/readers for more resources
From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and advances in Artificial Intelligence (AI) are paving the way to develop a systems view of cancer. However, the co-existence of big data and small data resources in this biomedical area highlights the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research, emphasizing the necessity of advancing interoperability among AI approaches and discussing the synergy between discriminative and generative models with examples of techniques and applications.
From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available