Abstract
Transcription profiling enables researchers to understand the activity of the genes in various experimental conditions; in human genomics, abnormal gene expression is typically correlated with clinical conditions. An important application is the detection of genes which are most involved in the development of tumors, by contrasting normal and tumor cells of the same patient. Several statistical and machine learning techniques have been applied to cancer detection; more recently, deep learning methods have been attempted, but they have typically failed in meeting the same performance as classical algorithms. In this paper, we design a set of deep learning methods that can achieve similar performance as the best machine learning methods thanks to the use of external information or of data augmentation; we demonstrate this result by comparing the performance of new methods against several baselines.
A. Canakoglu and L. Nanni—Co-primary authors.
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Acknowledgment
This work is supported by the ERC Advanced Grant 693174
(Data-Driven Genomic Computing) and by the Amazon Machine Learning Research Award on Data-driven Machine and Deep Learning for Genomics.
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Canakoglu, A., Nanni, L., Sokolovsky, A., Ceri, S. (2020). Designing and Evaluating Deep Learning Models for Cancer Detection on Gene Expression Data. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_22
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