Abstract
The application of deep learning to biology is of increasing relevance, but it is difficult; one of the main difficulties is the lack of massive amounts of training data. However, some recent applications of deep learning to the classification of labeled cancer datasets have been successful. Along this direction, in this paper, we apply Ladder networks, a recent and interesting network model, to the binary cancer classification problem; our results improve over the state of the art in deep learning and over the conventional state of the art in machine learning; achieving such results required a careful adaptation of the available datasets and tuning of the network.
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Acknowledgment
This work was supported by the ERC Advanced Grant GeCo (Data-Driven Genomic Computing) (Grant No. 693174) awarded to Prof. Stefano Ceri.
We thank Prof. Stefano Ceri who provided insight and expertise that greatly assisted the research and comments that greatly improved the manuscript.
We would like to thank also members of the GeCo project for helpful insights.
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Golcuk, G., Tuncel, M.A., Canakoglu, A. (2018). Exploiting Ladder Networks for Gene Expression Classification. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_23
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DOI: https://doi.org/10.1007/978-3-319-78723-7_23
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