Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics
Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the maj...
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| Materiálatiipa: | Online |
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Frontiers Media SA
2021
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| Liŋkkat: | 18729 |
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| author | Frank Emmert-Streib Benjamin Haibe-Kains |
| author_browse | Benjamin Haibe-Kains Frank Emmert-Streib |
| author_facet | Frank Emmert-Streib Benjamin Haibe-Kains |
| author_sort | Frank Emmert-Streib |
| collection | Directory of Open Access Books |
| description | Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks. |
| format | Online |
| id | doab-20.500.12854ir-57438 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media SA |
| publisherStr | Frontiers Media SA |
| record_format | ojs |
| spelling | doab-20.500.12854ir-574382024-04-05T12:34:54Z Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics Frank Emmert-Streib Benjamin Haibe-Kains QH426-470 TP248.13-248.65 TA1-2040 Q1-390 Validation Gene Expression Network Inference bioinformatics thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAK Genetics (non-medical) Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks. 2021-02-12T00:33:07Z 2021-02-12T00:33:07Z 2016-03-10 08:14:33 2015 book 18729 16648714 9782889194780 https://directory.doabooks.org/handle/20.500.12854/57438 eng Frontiers Research Topics image/jpeg Attribution 4.0 International http://www.frontiersin.org/books/Quantitative_Assessment_and_Validation_of_Network_Inference_Methods_in_Bioinformatics/488 http://journal.frontiersin.org/researchtopic/1216/quantitative-assessment-and-validation-of-network-inference-methods-in-bioinformatics Frontiers Media SA 10.3389/978-2-88919-478-0 10.3389/978-2-88919-478-0 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889194780 191 open access |
| spellingShingle | QH426-470 TP248.13-248.65 TA1-2040 Q1-390 Validation Gene Expression Network Inference bioinformatics thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAK Genetics (non-medical) Frank Emmert-Streib Benjamin Haibe-Kains Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics |
| title | Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics |
| title_full | Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics |
| title_fullStr | Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics |
| title_full_unstemmed | Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics |
| title_short | Quantitative Assessment and Validation of Network Inference Methods in Bioinformatics |
| title_sort | quantitative assessment and validation of network inference methods in bioinformatics |
| topic | QH426-470 TP248.13-248.65 TA1-2040 Q1-390 Validation Gene Expression Network Inference bioinformatics thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAK Genetics (non-medical) |
| topic_facet | QH426-470 TP248.13-248.65 TA1-2040 Q1-390 Validation Gene Expression Network Inference bioinformatics thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAK Genetics (non-medical) |
| url | 18729 |
| work_keys_str_mv | AT frankemmertstreib quantitativeassessmentandvalidationofnetworkinferencemethodsinbioinformatics AT benjaminhaibekains quantitativeassessmentandvalidationofnetworkinferencemethodsinbioinformatics |