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Deep Large-Scale Multitask Learning Network for Gene Expression Inference
- Source :
- J Comput Biol
- Publication Year :
- 2021
-
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.
- Subjects :
- Computer science
Inference
Multi-task learning
Latent variable
Overfitting
Machine learning
computer.software_genre
Genome
Machine Learning
Genetics
Profiling (information science)
Gene Regulatory Networks
Molecular Biology
Models, Genetic
business.industry
Gene Expression Profiling
Computational Biology
Regression analysis
Expression (mathematics)
Computational Mathematics
ComputingMethodologies_PATTERNRECOGNITION
Computational Theory and Mathematics
Gene Expression Regulation
Modeling and Simulation
Artificial intelligence
Preface
business
computer
Algorithms
Subjects
Details
- ISSN :
- 15578666
- Volume :
- 28
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- Journal of computational biology : a journal of computational molecular cell biology
- Accession number :
- edsair.doi.dedup.....83ab254ff472b658aff8df5251283c4c