دورية أكاديمية
Multi-study inference of regulatory networks for more accurate models of gene regulation.
| العنوان: | Multi-study inference of regulatory networks for more accurate models of gene regulation. |
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| المؤلفون: | Dayanne M Castro, Nicholas R de Veaux, Emily R Miraldi, Richard Bonneau |
| المصدر: | PLoS Computational Biology, Vol 15, Iss 1, p e1006591 (2019) |
| بيانات النشر: | Public Library of Science (PLoS), 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Biology (General) |
| مصطلحات موضوعية: | Biology (General), QH301-705.5 |
| الوصف: | Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1553-734X 1553-7358 35146478 |
| Relation: | https://doaj.org/toc/1553-734X; https://doaj.org/toc/1553-7358 |
| DOI: | 10.1371/journal.pcbi.1006591 |
| URL الوصول: | https://doaj.org/article/8601b05fb3514647844886f1952d35e8 |
| رقم الأكسشن: | edsdoj.8601b05fb3514647844886f1952d35e8 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 1553734X 15537358 35146478 |
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| DOI: | 10.1371/journal.pcbi.1006591 |