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Deep Large-Scale Multi-task Learning Network for Gene Expression Inference

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Book cover Research in Computational Molecular Biology (RECOMB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12074))

Abstract

Gene expressions profiling empowers many biological studies in various fields by comprehensive characterization of cellular status under different experimental conditions. Despite the recent advances in high-throughput technologies, profiling the whole-genome set is still challenging and expensive. Based on the fact that there is high correlation among the expression patterns of different genes, the above issue can be addressed by a cost-effective approach that collects only a small subset of genes, called landmark genes, as the representative of the entire genome set and estimates the remaining ones, called target genes, via the computational model. Several shallow and deep regression models have been presented in the literature for inferring the expressions of target genes. However, the shallow models suffer from underfitting due to their insufficient capacity in capturing the complex nature of gene expression data, and the existing deep models are prone to overfitting due to the lack of using the interrelations of target genes in the learning framework. To address these challenges, we formulate the gene expression inference as a multi-task learning problem and propose a novel deep multi-task learning algorithm with automatically learning the biological interrelations among target genes and utilizing such information to enhance the prediction. In particular, we employ a multi-layer sub-network with low dimensional latent variables for learning the interrelations among target genes (i.e. distinct predictive tasks), and impose a seamless and easy to implement regularization on deep models. Unlike the conventional complicated multi-task learning methods, which can only deal with tens or hundreds of tasks, our proposed algorithm can effectively learn the interrelations from the large-scale (\(\sim \)10,000) tasks on the gene expression inference problem, and does not suffer from cost-prohibitive operations. Experimental results indicate the superiority of our method compared to the existing gene expression inference models and alternative multi-task learning algorithms on two large-scale datasets.

This work was partially supported by NSF IIS 1836938, DBI 1836866, IIS 1845666, IIS 1852606, IIS 1838627, IIS 1837956, and NIH AG049371.

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Notes

  1. 1.

    http://www.lincsproject.org/.

  2. 2.

    https://cbcl.ics.uci.edu/public_data/D-GEX/.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA

    Kamran Ghasedi Dizaji & Heng Huang

  2. Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, USA

    Heng Huang

  3. Department of Pediatrics, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, USA

    Wei Chen

Authors
  1. Kamran Ghasedi Dizaji
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  2. Wei Chen
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  3. Heng Huang
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Correspondence to Heng Huang .

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Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Russell Schwartz

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Dizaji, K.G., Chen, W., Huang, H. (2020). Deep Large-Scale Multi-task Learning Network for Gene Expression Inference. In: Schwartz, R. (eds) Research in Computational Molecular Biology. RECOMB 2020. Lecture Notes in Computer Science(), vol 12074. Springer, Cham. https://doi.org/10.1007/978-3-030-45257-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-45257-5_2

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