Erratum to: New determination of the astrophysical 13 C( p, $ \gamma$ 14 N S(E) factors and reaction rates via the 13 C( 7 Li, 6 He) 14 N reaction

Li, Y. J.; Li, Z. H.; Li, E. T.; Bai, X. X.; Su, J.; Wang, B. X.; Yan, S. Q.; Zeng, Sheng; Li, Z. C.; Liu, J. C.; Liu, X.; Jin, S. J.; Wang, Y. B.; Zhang, L. Y.; Yu, Xinyang; Li, L.; Liu, Gang; Fan, Q. W.; Liu, W. P.

doi:10.1140/epja/i2013-13106-8

Cited by 3 publications

(9 citation statements)

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“…We compute the area under the receiver operating characteristic curves (AUROC) for each reverse-engineered network inferred from differently discretized data. As seen in Figure 1 AUROC for mean discretization is slightly higher than what was reported in (Li et al, 2014) (by 0.0475), but our area under the precision-recall curve (0.0118) is almost the same with the value (0.0155) reported in (Li et al, 2014).…”

Section: Dream3 Yeast In Silicosupporting

confidence: 66%

“…TDT and max75 also improve TDBN performance, compared to when using mean discretization, the discretization utilized in (Li et al, 2014;Zou and Conzen, 2005). TDBN with input from Top75 discretization shows an increase of 1.6% AUROC compared to TDBN fed with binary data obtained from mean discretization, which is utilized in (Li et al, 2014). Further analysis shows that the higher AUROC of top75 is due to the inference of fewer false positives compared to mean discretization (Table S3).…”

Section: Dream3 Yeast In Silicomentioning

confidence: 94%

“…We observed that Top75 is the data discretization that makes TDBN perform best. TDT and max75 also improve TDBN performance, compared to when using mean discretization, the discretization utilized in (Li et al, 2014;Zou and Conzen, 2005). TDBN with input from Top75 discretization shows an increase of 1.6% AUROC compared to TDBN fed with binary data obtained from mean discretization, which is utilized in (Li et al, 2014).…”

Section: Dream3 Yeast In Silicomentioning

confidence: 96%

“…The gold standard of this network is known. We examine how data discretization influences the accuracy of time-delayed dynamic Bayesian network (TDBN), originally introduced in (Zou and Conzen, 2005) and later applied in (Li et al, 2014) to infer the DREAM3 yeast in silico network. Since TDBN requires binary input data, we select all 11 discretization methods in GED PRO TOOLS capable of binary discretization (bikmeans2, i2, kmeans2, max25, max50, max75, mean, q2, TDT, top25, top75) for comparison.…”

Section: Dream3 Yeast In Silico Networkmentioning

confidence: 99%

“…We then use different data discretization methods from GED PRO TOOLS. In (Li et al, 2014;Zou and Conzen, 2005) the authors used the equivalent of the mean discretization method in GED PRO TOOLS (threshold by the mean of each variable) before inferring the network using TDBN.…”

Section: Dream3 Yeast In Silicomentioning

confidence: 99%

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Benchmarking time-series data discretization on inference methods

Jann

Vera-Licona

2019

Bioinformatics

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Summary The rapid development in quantitatively measuring DNA, RNA and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider four published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods’ performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed. Availability and implementation All the datasets, reverse-engineering methods and source code used in this paper are available in Vera-Licona’s lab Github repository: https://github.com/VeraLiconaResearchGroup/Benchmarking_TSDiscretizations. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Dream3 Yeast In Silicosupporting

confidence: 66%

Section: Dream3 Yeast In Silicomentioning

confidence: 94%

Section: Dream3 Yeast In Silicomentioning

confidence: 96%

Section: Dream3 Yeast In Silico Networkmentioning

confidence: 99%

Section: Dream3 Yeast In Silicomentioning

confidence: 99%

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Benchmarking time-series data discretization on inference methods

Jann

Vera-Licona

2019

Bioinformatics

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Copper and Copper Complexes in Tumor Therapy

Wang,

Tang,

Yuan

et al. 2024

ChemMedChem

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Copper (Cu), a crucial trace element in physiological processes, has garnered significant interest for its involvement in cancer progression and potential therapeutic applications. The regulation of cellular copper levels is essential for maintaining copper homeostasis, as imbalances can lead to toxicity and cell death. The development of drugs that target copper homeostasis has emerged as a promising strategy for anticancer treatment, with a particular focus on copper chelators, copper ionophores, and novel copper complexes. Recent research has also investigated the potential of copper complexes in cancer therapy.

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