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Showing 1837 to 1842 of 1842 entries
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Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently.

The Biochemical journal

Kell DB, Samanta S, Swainston N.
PMID: 33290527
Biochem J. 2020 Dec 11;477(23):4559-4580. doi: 10.1042/BCJ20200781.

The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have...

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Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J. 2020;477(23):4559-4580doi: 10.1042/BCJ20200781.
Kell, D. B., Samanta, S., & Swainston, N. (2020). Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. The Biochemical journal, 477(23), 4559-4580. https://doi.org/10.1042/BCJ20200781
Kell, Douglas B, et al. "Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently." The Biochemical journal vol. 477,23 (2020): 4559-4580. doi: https://doi.org/10.1042/BCJ20200781
Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J. 2020 Dec 11;477(23):4559-4580. doi: 10.1042/BCJ20200781. PMID: 33290527; PMCID: PMC7733676.
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Multithreshold change plane model: Estimation theory and applications in subgroup identification.

Statistics in medicine

Li J, Li Y, Jin B, Kosorok MR.
PMID: 33843100
Stat Med. 2021 Jul 10;40(15):3440-3459. doi: 10.1002/sim.8976. Epub 2021 Apr 11.

We propose a multithreshold change plane regression model which naturally partitions the observed subjects into subgroups with different covariate effects. The underlying grouping variable is a linear function of observed covariates and thus multiple thresholds produce change planes in...

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Li J, Li Y, Jin B, et al. Multithreshold change plane model: Estimation theory and applications in subgroup identification. Stat Med. 2021;40(15):3440-3459doi: 10.1002/sim.8976.
Li, J., Li, Y., Jin, B., & Kosorok, M. R. (2021). Multithreshold change plane model: Estimation theory and applications in subgroup identification. Statistics in medicine, 40(15), 3440-3459. https://doi.org/10.1002/sim.8976
Li, Jialiang, et al. "Multithreshold change plane model: Estimation theory and applications in subgroup identification." Statistics in medicine vol. 40,15 (2021): 3440-3459. doi: https://doi.org/10.1002/sim.8976
Li J, Li Y, Jin B, Kosorok MR. Multithreshold change plane model: Estimation theory and applications in subgroup identification. Stat Med. 2021 Jul 10;40(15):3440-3459. doi: 10.1002/sim.8976. Epub 2021 Apr 11. PMID: 33843100; PMCID: PMC8180507.
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Do Response Styles Affect Estimates of Growth on Social-Emotional Constructs? Evidence from Four Years of Longitudinal Survey Scores.

Multivariate behavioral research

Soland J, Kuhfeld M.
PMID: 32633574
Multivariate Behav Res. 2021 Nov-Dec;56(6):853-873. doi: 10.1080/00273171.2020.1778440. Epub 2020 Jul 07.

Survey respondents employ different response styles when they use the categories of the Likert scale differently despite having the same true score on the construct of interest. For example, respondents may be more likely to use the extremes of...

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Soland J, Kuhfeld M. Do Response Styles Affect Estimates of Growth on Social-Emotional Constructs? Evidence from Four Years of Longitudinal Survey Scores. Multivariate Behav Res. 2020;56(6):853-873doi: 10.1080/00273171.2020.1778440.
Soland, J., & Kuhfeld, M. (2021). Do Response Styles Affect Estimates of Growth on Social-Emotional Constructs? Evidence from Four Years of Longitudinal Survey Scores. Multivariate behavioral research, 56(6), 853-873. https://doi.org/10.1080/00273171.2020.1778440
Soland, James, and Kuhfeld, Megan. "Do Response Styles Affect Estimates of Growth on Social-Emotional Constructs? Evidence from Four Years of Longitudinal Survey Scores." Multivariate behavioral research vol. 56,6 (2021): 853-873. doi: https://doi.org/10.1080/00273171.2020.1778440
Soland J, Kuhfeld M. Do Response Styles Affect Estimates of Growth on Social-Emotional Constructs? Evidence from Four Years of Longitudinal Survey Scores. Multivariate Behav Res. 2021 Nov-Dec;56(6):853-873. doi: 10.1080/00273171.2020.1778440. Epub 2020 Jul 07. PMID: 32633574.
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Effect of radial distribution of injected flow on simulated moving bed performance.

Journal of chromatography. A

Kim Y, Cho S, Jang K, Lee J, Kim M, Moon I.
PMID: 34906766
J Chromatogr A. 2022 Jan 11;1662:462703. doi: 10.1016/j.chroma.2021.462703. Epub 2021 Nov 29.

In the modeling of a simulated moving bed, several assumptions are considered, the key assumption is there are no radial concentration gradients based on perfect mixing. However, it is difficult to achieve perfect mixing because the injected flowrate of...

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Kim Y, Cho S, Jang K, et al. Effect of radial distribution of injected flow on simulated moving bed performance. J Chromatogr A. 2021;1662:462703doi: 10.1016/j.chroma.2021.462703.
Kim, Y., Cho, S., Jang, K., Lee, J., Kim, M., & Moon, I. (2022). Effect of radial distribution of injected flow on simulated moving bed performance. Journal of chromatography. A, 1662462703. https://doi.org/10.1016/j.chroma.2021.462703
Kim, Youngjin, et al. "Effect of radial distribution of injected flow on simulated moving bed performance." Journal of chromatography. A vol. 1662 (2022): 462703. doi: https://doi.org/10.1016/j.chroma.2021.462703
Kim Y, Cho S, Jang K, Lee J, Kim M, Moon I. Effect of radial distribution of injected flow on simulated moving bed performance. J Chromatogr A. 2022 Jan 11;1662:462703. doi: 10.1016/j.chroma.2021.462703. Epub 2021 Nov 29. PMID: 34906766.
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Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation.

Annals of epidemiology

Dunne J, Tessema GA, Ognjenovic M, Pereira G.
PMID: 34384883
Ann Epidemiol. 2021 Nov;63:86-101. doi: 10.1016/j.annepidem.2021.07.033. Epub 2021 Aug 09.

PURPOSE: The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the...

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Dunne J, Tessema GA, Ognjenovic M, et al. Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation. Ann Epidemiol. 2021;63:86-101doi: 10.1016/j.annepidem.2021.07.033.
Dunne, J., Tessema, G. A., Ognjenovic, M., & Pereira, G. (2021). Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation. Annals of epidemiology, 6386-101. https://doi.org/10.1016/j.annepidem.2021.07.033
Dunne, Jennifer, et al. "Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation." Annals of epidemiology vol. 63 (2021): 86-101. doi: https://doi.org/10.1016/j.annepidem.2021.07.033
Dunne J, Tessema GA, Ognjenovic M, Pereira G. Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation. Ann Epidemiol. 2021 Nov;63:86-101. doi: 10.1016/j.annepidem.2021.07.033. Epub 2021 Aug 09. PMID: 34384883.
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Exploring dynamic metabolomics data with multiway data analysis: a simulation study.

BMC bioinformatics

Li L, Hoefsloot H, de Graaf AA, Acar E, Smilde AK.
PMID: 35012453
BMC Bioinformatics. 2022 Jan 10;23(1):31. doi: 10.1186/s12859-021-04550-5.

BACKGROUND: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can...

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Li L, Hoefsloot H, de Graaf AA, et al. Exploring dynamic metabolomics data with multiway data analysis: a simulation study. BMC Bioinformatics. 2022;23(1):31doi: 10.1186/s12859-021-04550-5.
Li, L., Hoefsloot, H., de Graaf, A. A., Acar, E., & Smilde, A. K. (2022). Exploring dynamic metabolomics data with multiway data analysis: a simulation study. BMC bioinformatics, 23(1), 31. https://doi.org/10.1186/s12859-021-04550-5
Li, Lu, et al. "Exploring dynamic metabolomics data with multiway data analysis: a simulation study." BMC bioinformatics vol. 23,1 (2022): 31. doi: https://doi.org/10.1186/s12859-021-04550-5
Li L, Hoefsloot H, de Graaf AA, Acar E, Smilde AK. Exploring dynamic metabolomics data with multiway data analysis: a simulation study. BMC Bioinformatics. 2022 Jan 10;23(1):31. doi: 10.1186/s12859-021-04550-5. PMID: 35012453; PMCID: PMC8750750.
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Showing 1837 to 1842 of 1842 entries