Abstract
With recent advances in high-throughput sequencing, reading the human genome is not an arduous task anymore. The extensive collection of different types of omics data and possible causal relations between them have led the scientists to exploit specialized machine learning methods such as deep learning and perform integrative analysis of multi-source datasets. In this paper, we compare the performance of both generative and discriminative deep models based on their integration stage. First, we explain the architecture and mathematical point of view of these methods. Then we evaluate the performances of different models by applying them on two sets of cancer-related data to discover the effect of the integration stage on classification accuracy.
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Khoshghalbvash, F., Gao, J.X. (2020). The Effect of Integration Stage on Multimodal Deep Learning in Genomic Studies. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_39
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DOI: https://doi.org/10.1007/978-981-13-9409-6_39
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