Abstract
Recent advances in scientific research point out that diagnostic prediction represents a novel paradigm because of the decreased expense and the expanded productivity of multi-omics technologies such as gene expression profiling. In order to evaluate a mammoth amount of biomarkers produced by high-throughput technologies, machine learning and predictive approaches such as artificial neural network (ANN) algorithms have widely been utilized to assess disease mechanisms and intervention outcomes. In this chapter, we first illustrated ANN algorithms for establishing biomarkers in diagnostic prediction studies. We then surveyed a variety of diagnostic prediction applications for numerous diseases and treatments with consideration of ANN algorithms and gene expression profiling. Finally, we outlined their limitations and future directions. Future work in diagnostic prediction studies promises to lead to innovative ideas related to disease prevention and drug responsiveness in light of multi-omics technologies as well as machine learning and predictive algorithms.
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Acknowledgments
The authors extend their sincere thanks to Vita Genomics, Inc. and SBIR grants (S099000280249-154) from the Department of Economic Affairs in Taiwan for funding this research.
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Lin, E., Tsai, SJ. (2018). Diagnostic Prediction Based on Gene Expression Profiles and Artificial Neural Networks. In: Purohit, H., Kalia, V., More, R. (eds) Soft Computing for Biological Systems. Springer, Singapore. https://doi.org/10.1007/978-981-10-7455-4_2
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DOI: https://doi.org/10.1007/978-981-10-7455-4_2
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