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  • 1
    Online Resource
    Online Resource
    DataSheet1.PDF
    Frontiers Media SA ; 2022
    In:  Frontiers in Genetics Vol. 13 ( 2022-7-13)
    Details
    In: Frontiers in Genetics, Frontiers Media SA, Vol. 13 ( 2022-7-13)
    Abstract: Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed that, under suitable data conditions, perform well in benchmarks that consider the entire spectrum of false-positives and -negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN. Lacking criteria for sparsity selection, a simplistic solution is to pick the GRN that has a certain number of links per gene, which is guessed to be reasonable. However, this does not guarantee finding the GRN that has the correct sparsity or is the most accurate one. In this study, we provide a general approach for identifying the most accurate and sparsity-wise relevant GRN within the entire space of possible GRNs. The algorithm, called SPA, applies a “GRN information criterion” (GRNIC) that is inspired by two commonly used model selection criteria, Akaike and Bayesian Information Criterion (AIC and BIC) but adapted to GRN inference. The results show that the approach can, in most cases, find the GRN whose sparsity is close to the true sparsity and close to as accurate as possible with the given GRN inference method and data. The datasets and source code can be found at https://bitbucket.org/sonnhammergrni/spa/ .
    Type of Medium: Online Resource
    ISSN: 1664-8021
    URL: Article
    URL: Article
    DOI: 10.3389/fgene.2022.855770
    DOI: 10.3389/fgene.2022.855770.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606823-0
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  • 2
    Online Resource
    Online Resource
    Knowledge of the perturbation design is essential for accurate gene regulatory network inference
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-10-03)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-10-03)
    Abstract: The gene regulatory network (GRN) of a cell executes genetic programs in response to environmental and internal cues. Two distinct classes of methods are used to infer regulatory interactions from gene expression: those that only use observed changes in gene expression, and those that use both the observed changes and the perturbation design, i.e. the targets used to cause the changes in gene expression. Considering that the GRN by definition converts input cues to changes in gene expression, it may be conjectured that the latter methods would yield more accurate inferences but this has not previously been investigated. To address this question, we evaluated a number of popular GRN inference methods that either use the perturbation design or not. For the evaluation we used targeted perturbation knockdown gene expression datasets with varying noise levels generated by two different packages, GeneNetWeaver and GeneSpider. The accuracy was evaluated on each dataset using a variety of measures. The results show that on all datasets, methods using the perturbation design matrix consistently and significantly outperform methods not using it. This was also found to be the case on a smaller experimental dataset from E. coli . Targeted gene perturbations combined with inference methods that use the perturbation design are indispensable for accurate GRN inference.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-022-19005-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 3
    Online Resource
    Online Resource
    Inferring the experimental design for accurate gene regulatory network inference
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 20 ( 2021-10-25), p. 3553-3559
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 20 ( 2021-10-25), p. 3553-3559
    Abstract: Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems, such as experimental noise or off-target effects, limiting the methods’ ability to reconstruct the true GRN. Results In this study, we propose an algorithm, IDEMAX, to infer the effective perturbation design from gene expression data in order to eliminate the potential risk of fitting a disconnected perturbation design to gene expression. We applied IDEMAX to synthetic data from two different data generation tools, GeneNetWeaver and GeneSPIDER, and assessed its effect on the experiment design matrix as well as the accuracy of the GRN inference, followed by application to a real dataset. The results show that our approach consistently improves the accuracy of GRN inference compared to using the intended perturbation design when much of the signal is hidden by noise, which is often the case for real data. Availability and implementation https://bitbucket.org/sonnhammergrni/idemax. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btab367
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    GRNbenchmark - a web server for benchmarking directed gene regulatory network inference methods
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. W1 ( 2022-07-05), p. W398-W404
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. W1 ( 2022-07-05), p. W398-W404
    Abstract: Accurate inference of gene regulatory networks (GRN) is an essential component of systems biology, and there is a constant development of new inference methods. The most common approach to assess accuracy for publications is to benchmark the new method against a selection of existing algorithms. This often leads to a very limited comparison, potentially biasing the results, which may stem from tuning the benchmark's properties or incorrect application of other methods. These issues can be avoided by a web server with a broad range of data properties and inference algorithms, that makes it easy to perform comprehensive benchmarking of new methods, and provides a more objective assessment. Here we present https://GRNbenchmark.org/ - a new web server for benchmarking GRN inference methods, which provides the user with a set of benchmarks with several datasets, each spanning a range of properties including multiple noise levels. As soon as the web server has performed the benchmarking, the accuracy results are made privately available to the user via interactive summary plots and underlying curves. The user can then download these results for any purpose, and decide whether or not to make them public to share with the community.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkac377
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Early post-partum diabetes mellitus screening rates in patients with history of gestational diabetes
    Springer Science and Business Media LLC ; 2010
    In:  Archives of Gynecology and Obstetrics Vol. 282, No. 6 ( 2010-12), p. 613-616
    Details
    In: Archives of Gynecology and Obstetrics, Springer Science and Business Media LLC, Vol. 282, No. 6 ( 2010-12), p. 613-616
    Type of Medium: Online Resource
    ISSN: 0932-0067 , 1432-0711
    URL: Article
    DOI: 10.1007/s00404-009-1258-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2010
    detail.hit.zdb_id: 1458450-5
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  • 6
    Online Resource
    Online Resource
    Incidence of diabetes mellitus at postpartum six to twelve months following the diagnosis of gestational diabetes mellitus
    Galenos Yayinevi ; 2010
    In:  Journal of the Turkish German Gynecological Association Vol. 11, No. 2 ( 2010-6-1), p. 89-94
    Details
    In: Journal of the Turkish German Gynecological Association, Galenos Yayinevi, Vol. 11, No. 2 ( 2010-6-1), p. 89-94
    Type of Medium: Online Resource
    ISSN: 1309-0399 , 1309-0380
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.5152/jtgga.2010
    DOI: 10.5152/jtgga
    DOI: 10.5152/jtgga.2010.06
    Language: Unknown
    Publisher: Galenos Yayinevi
    Publication Date: 2010
    detail.hit.zdb_id: 2425806-4
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  • 7
    Online Resource
    Online Resource
    Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data
    Springer Science and Business Media LLC ; 2020
    In:  npj Systems Biology and Applications Vol. 6, No. 1 ( 2020-11-09)
    Details
    In: npj Systems Biology and Applications, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2020-11-09)
    Abstract: The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.
    Type of Medium: Online Resource
    ISSN: 2056-7189
    URL: Article
    DOI: 10.1038/s41540-020-00154-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2841868-2
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  • 8
    Online Resource
    Online Resource
    Fast and accurate gene regulatory network inference by normalized least squares regression
    Oxford University Press (OUP) ; 2022
    In:  Bioinformatics Vol. 38, No. 8 ( 2022-04-12), p. 2263-2268
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 8 ( 2022-04-12), p. 2263-2268
    Abstract: Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The Least Squares Cut-Off (LSCO) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes. Results We developed a GRN inference method called Least Squares Cut-Off with Normalization (LSCON) that tackles this problem. LSCON extends the LSCO algorithm by regularization to avoid hyper-connected genes and thereby reduce false positives. The regularization used is based on normalization, which removes effects of extreme values on the fit. We benchmarked LSCON and compared it to Genie3, LASSO, LSCO and Ridge regression, in terms of accuracy, speed and tendency to predict hyper-connected genes. The results show that LSCON achieves better or equal accuracy compared to LASSO, the best existing method, especially for data with extreme values. Thanks to the speed of least squares regression, LSCON does this an order of magnitude faster than LASSO. Availability and implementation Data: https://bitbucket.org/sonnhammergrni/lscon; Code: https://bitbucket.org/sonnhammergrni/genespider. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btac103
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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