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Stability of controlled biological systems.

Journal of theoretical biology

Walter C.
PMID: 5798216
J Theor Biol. 1969 Apr;23(1):23-38. doi: 10.1016/0022-5193(69)90065-4.

No abstract available.

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Walter C. Stability of controlled biological systems. J Theor Biol. 1969;23(1):23-38doi: 10.1016/0022-5193(69)90065-4.
Walter, C. (1969). Stability of controlled biological systems. Journal of theoretical biology, 23(1), 23-38. https://doi.org/10.1016/0022-5193(69)90065-4
Walter, C. "Stability of controlled biological systems." Journal of theoretical biology vol. 23,1 (1969): 23-38. doi: https://doi.org/10.1016/0022-5193(69)90065-4
Walter C. Stability of controlled biological systems. J Theor Biol. 1969 Apr;23(1):23-38. doi: 10.1016/0022-5193(69)90065-4. PMID: 5798216.
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Bounds on the populations in the Dreitlein-Smoes model of oscillatory kinetic systems.

Bulletin of mathematical biology

Fizell RG, Rubin PE.
PMID: 990546
Bull Math Biol. 1976;38(6):623-31. doi: 10.1007/BF02458637.

No abstract available.

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Fizell RG, Rubin PE. Bounds on the populations in the Dreitlein-Smoes model of oscillatory kinetic systems. Bull Math Biol. 1976;38(6):623-31doi: 10.1007/BF02458637.
Fizell, R. G., & Rubin, P. E. (1976). Bounds on the populations in the Dreitlein-Smoes model of oscillatory kinetic systems. Bulletin of mathematical biology, 38(6), 623-31. https://doi.org/10.1007/BF02458637
Fizell, R G, and Rubin, P E. "Bounds on the populations in the Dreitlein-Smoes model of oscillatory kinetic systems." Bulletin of mathematical biology vol. 38,6 (1976): 623-31. doi: https://doi.org/10.1007/BF02458637
Fizell RG, Rubin PE. Bounds on the populations in the Dreitlein-Smoes model of oscillatory kinetic systems. Bull Math Biol. 1976;38(6):623-31. doi: 10.1007/BF02458637. PMID: 990546.
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Critical points of the O(n) loop model on the martini and the 3-12 lattices.

Physical review. E, Statistical, nonlinear, and soft matter physics

Ding C, Fu Z, Guo W.
PMID: 23005148
Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Jun;85(6):062101. doi: 10.1103/PhysRevE.85.062101. Epub 2012 Jun 19.

We derive the critical line of the O(n) loop model on the martini lattice as a function of the loop weight n basing on the critical points on the honeycomb lattice conjectured by Nienhuis [Phys. Rev. Lett. 49, 1062...

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Ding C, Fu Z, Guo W. Critical points of the O(n) loop model on the martini and the 3-12 lattices. Phys Rev E Stat Nonlin Soft Matter Phys. 2012;85(6):062101doi: 10.1103/PhysRevE.85.062101.
Ding, C., Fu, Z., & Guo, W. (2012). Critical points of the O(n) loop model on the martini and the 3-12 lattices. Physical review. E, Statistical, nonlinear, and soft matter physics, 85(6), 062101. https://doi.org/10.1103/PhysRevE.85.062101
Ding, Chengxiang, et al. "Critical points of the O(n) loop model on the martini and the 3-12 lattices." Physical review. E, Statistical, nonlinear, and soft matter physics vol. 85,6 (2012): 062101. doi: https://doi.org/10.1103/PhysRevE.85.062101
Ding C, Fu Z, Guo W. Critical points of the O(n) loop model on the martini and the 3-12 lattices. Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Jun;85(6):062101. doi: 10.1103/PhysRevE.85.062101. Epub 2012 Jun 19. PMID: 23005148.
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Machine Learning Estimation of Atom Condensed Fukui Functions.

Molecular informatics

Zhang Q, Zheng F, Zhao T, Qu X, Aires-de-Sousa J.
PMID: 27491791
Mol Inform. 2016 Feb;35(2):62-9. doi: 10.1002/minf.201500113. Epub 2015 Nov 24.

To enable the fast estimation of atom condensed Fukui functions, machine learning algorithms were trained with databases of DFT pre-calculated values for ca. 23,000 atoms in organic molecules. The problem was approached as the ranking of atom types with...

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Zhang Q, Zheng F, Zhao T, et al. Machine Learning Estimation of Atom Condensed Fukui Functions. Mol Inform. 2015;35(2):62-9doi: 10.1002/minf.201500113.
Zhang, Q., Zheng, F., Zhao, T., Qu, X., & Aires-de-Sousa, J. (2016). Machine Learning Estimation of Atom Condensed Fukui Functions. Molecular informatics, 35(2), 62-9. https://doi.org/10.1002/minf.201500113
Zhang, Qingyou, et al. "Machine Learning Estimation of Atom Condensed Fukui Functions." Molecular informatics vol. 35,2 (2016): 62-9. doi: https://doi.org/10.1002/minf.201500113
Zhang Q, Zheng F, Zhao T, Qu X, Aires-de-Sousa J. Machine Learning Estimation of Atom Condensed Fukui Functions. Mol Inform. 2016 Feb;35(2):62-9. doi: 10.1002/minf.201500113. Epub 2015 Nov 24. PMID: 27491791.
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Molecular Simulations.

Methods of biochemical analysis

[No authors listed]
PMID: 26173319
Methods Biochem Anal. 2015;55:357-81. doi: 10.1002/9781118859148.ch16.

No abstract available.

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Molecular Simulations. Methods Biochem Anal. 2015;55:357-81doi: 10.1002/9781118859148.ch16.
(2015). Molecular Simulations. Methods of biochemical analysis, 55357-81. https://doi.org/10.1002/9781118859148.ch16
"Molecular Simulations." Methods of biochemical analysis vol. 55 (2015): 357-81. doi: https://doi.org/10.1002/9781118859148.ch16
Molecular Simulations. Methods Biochem Anal. 2015;55:357-81. doi: 10.1002/9781118859148.ch16. PMID: 26173319.
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Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space.

Scientific reports

Peón A, Naulaerts S, Ballester PJ.
PMID: 28630414
Sci Rep. 2017 Jun 19;7(1):3820. doi: 10.1038/s41598-017-04264-w.

Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively...

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Peón A, Naulaerts S, Ballester PJ. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Sci Rep. 2017;7(1):3820doi: 10.1038/s41598-017-04264-w.
Peón, A., Naulaerts, S., & Ballester, P. J. (2017). Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Scientific reports, 7(1), 3820. https://doi.org/10.1038/s41598-017-04264-w
Peón, Antonio, et al. "Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space." Scientific reports vol. 7,1 (2017): 3820. doi: https://doi.org/10.1038/s41598-017-04264-w
Peón A, Naulaerts S, Ballester PJ. Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space. Sci Rep. 2017 Jun 19;7(1):3820. doi: 10.1038/s41598-017-04264-w. PMID: 28630414; PMCID: PMC5476590.
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Large-scale dynamo growth rates from numerical simulations and implications for mean-field theories.

Physical review. E, Statistical, nonlinear, and soft matter physics

Park K, Blackman EG, Subramanian K.
PMID: 23767646
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 May;87(5):053110. doi: 10.1103/PhysRevE.87.053110. Epub 2013 May 28.

Understanding large-scale magnetic field growth in turbulent plasmas in the magnetohydrodynamic limit is a goal of magnetic dynamo theory. In particular, assessing how well large-scale helical field growth and saturation in simulations match those predicted by existing theories is...

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Park K, Blackman EG, Subramanian K. Large-scale dynamo growth rates from numerical simulations and implications for mean-field theories. Phys Rev E Stat Nonlin Soft Matter Phys. 2013;87(5):053110doi: 10.1103/PhysRevE.87.053110.
Park, K., Blackman, E. G., & Subramanian, K. (2013). Large-scale dynamo growth rates from numerical simulations and implications for mean-field theories. Physical review. E, Statistical, nonlinear, and soft matter physics, 87(5), 053110. https://doi.org/10.1103/PhysRevE.87.053110
Park, Kiwan, et al. "Large-scale dynamo growth rates from numerical simulations and implications for mean-field theories." Physical review. E, Statistical, nonlinear, and soft matter physics vol. 87,5 (2013): 053110. doi: https://doi.org/10.1103/PhysRevE.87.053110
Park K, Blackman EG, Subramanian K. Large-scale dynamo growth rates from numerical simulations and implications for mean-field theories. Phys Rev E Stat Nonlin Soft Matter Phys. 2013 May;87(5):053110. doi: 10.1103/PhysRevE.87.053110. Epub 2013 May 28. PMID: 23767646.
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Statistical mechanics of two-dimensional shuffled foams: geometry-topology correlation in small or large disorder limits.

Physical review. E, Statistical, nonlinear, and soft matter physics

Durand M, Kraynik AM, van Swol F, Käfer J, Quilliet C, Cox S, Ataei Talebi S, Graner F.
PMID: 25019778
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Jun;89(6):062309. doi: 10.1103/PhysRevE.89.062309. Epub 2014 Jun 19.

Bubble monolayers are model systems for experiments and simulations of two-dimensional packing problems of deformable objects. We explore the relation between the distributions of the number of bubble sides (topology) and the bubble areas (geometry) in the low liquid...

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Durand M, Kraynik AM, van Swol F, et al. Statistical mechanics of two-dimensional shuffled foams: geometry-topology correlation in small or large disorder limits. Phys Rev E Stat Nonlin Soft Matter Phys. 2014;89(6):062309doi: 10.1103/PhysRevE.89.062309.
Durand, M., Kraynik, A. M., van Swol, F., Käfer, J., Quilliet, C., Cox, S., Ataei Talebi, S., & Graner, F. (2014). Statistical mechanics of two-dimensional shuffled foams: geometry-topology correlation in small or large disorder limits. Physical review. E, Statistical, nonlinear, and soft matter physics, 89(6), 062309. https://doi.org/10.1103/PhysRevE.89.062309
Durand, Marc, et al. "Statistical mechanics of two-dimensional shuffled foams: geometry-topology correlation in small or large disorder limits." Physical review. E, Statistical, nonlinear, and soft matter physics vol. 89,6 (2014): 062309. doi: https://doi.org/10.1103/PhysRevE.89.062309
Durand M, Kraynik AM, van Swol F, Käfer J, Quilliet C, Cox S, Ataei Talebi S, Graner F. Statistical mechanics of two-dimensional shuffled foams: geometry-topology correlation in small or large disorder limits. Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Jun;89(6):062309. doi: 10.1103/PhysRevE.89.062309. Epub 2014 Jun 19. PMID: 25019778.
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[Formulation Optimization of Zuojin Floating-Bioadhesive Pellets by Central Composite Design-Response Surface Methodology].

Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials

Xu F, Wang LL, Shi ZQ, Chen F, Sun DM.
PMID: 26930991
Zhong Yao Cai. 2015 Sep;38(9):1969-73.

OBJECTIVE: To optimize the formulation of Zuojin floating-bioadhesive pellets by the central composite design-response surface methodology (CCD-RSM).METHODS: In the formulation design using CCD-RSM, independent variables were the amounts of sodium bicarbonate (X1), HPMC (X2) and MCC (X3) as factors....

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Xu F, Wang LL, Shi ZQ, et al. [Formulation Optimization of Zuojin Floating-Bioadhesive Pellets by Central Composite Design-Response Surface Methodology]. Zhong Yao Cai. 2015;38(9):1969-73
Xu, F., Wang, L. L., Shi, Z. Q., Chen, F., & Sun, D. M. (2015). [Formulation Optimization of Zuojin Floating-Bioadhesive Pellets by Central Composite Design-Response Surface Methodology]. Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials, 38(9), 1969-73.
Xu, Feng, et al. "[Formulation Optimization of Zuojin Floating-Bioadhesive Pellets by Central Composite Design-Response Surface Methodology]." Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials vol. 38,9 (2015): 1969-73.
Xu F, Wang LL, Shi ZQ, Chen F, Sun DM. [Formulation Optimization of Zuojin Floating-Bioadhesive Pellets by Central Composite Design-Response Surface Methodology]. Zhong Yao Cai. 2015 Sep;38(9):1969-73. Chinese. PMID: 26930991.
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An Additive Definition of Molecular Complexity.

Journal of chemical information and modeling

Böttcher T.
PMID: 26857537
J Chem Inf Model. 2016 Mar 28;56(3):462-70. doi: 10.1021/acs.jcim.5b00723. Epub 2016 Feb 22.

A framework for molecular complexity is established that is based on information theory and consistent with chemical knowledge. The resulting complexity index Cm is derived from abstracting the information content of a molecule by the degrees of freedom in...

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Böttcher T. An Additive Definition of Molecular Complexity. J Chem Inf Model. 2016;56(3):462-70doi: 10.1021/acs.jcim.5b00723.
Böttcher, T. (2016). An Additive Definition of Molecular Complexity. Journal of chemical information and modeling, 56(3), 462-70. https://doi.org/10.1021/acs.jcim.5b00723
Böttcher, Thomas. "An Additive Definition of Molecular Complexity." Journal of chemical information and modeling vol. 56,3 (2016): 462-70. doi: https://doi.org/10.1021/acs.jcim.5b00723
Böttcher T. An Additive Definition of Molecular Complexity. J Chem Inf Model. 2016 Mar 28;56(3):462-70. doi: 10.1021/acs.jcim.5b00723. Epub 2016 Feb 22. PMID: 26857537.
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Chemical geneticists unify their data.

Drug discovery today

Mandavilli A.
PMID: 12547009
Drug Discov Today. 2002 Dec 01;7(23):1148-9. doi: 10.1016/s1359-6446(02)02523-0.

No abstract available.

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Mandavilli A. Chemical geneticists unify their data. Drug Discov Today. 2002;7(23):1148-9doi: 10.1016/s1359-6446(02)02523-0.
Mandavilli, A. (2002). Chemical geneticists unify their data. Drug discovery today, 7(23), 1148-9. https://doi.org/10.1016/s1359-6446(02)02523-0
Mandavilli, Apoorva. "Chemical geneticists unify their data." Drug discovery today vol. 7,23 (2002): 1148-9. doi: https://doi.org/10.1016/s1359-6446(02)02523-0
Mandavilli A. Chemical geneticists unify their data. Drug Discov Today. 2002 Dec 01;7(23):1148-9. doi: 10.1016/s1359-6446(02)02523-0. PMID: 12547009.
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Multi-component reactions and evolutionary chemistry.

Drug discovery today

Weber L.
PMID: 11790626
Drug Discov Today. 2002 Jan 15;7(2):143-7. doi: 10.1016/s1359-6446(01)02090-6.

Multi-component reactions (MCRs) provide a new approach towards the efficient synthesis of diverse compounds and compound libraries. MCRs are evolving from being purely a chemistry curiosity to being recognized as having increasing relevance for drug discovery in terms of...

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Weber L. Multi-component reactions and evolutionary chemistry. Drug Discov Today. 2002;7(2):143-7doi: 10.1016/s1359-6446(01)02090-6.
Weber, L. (2002). Multi-component reactions and evolutionary chemistry. Drug discovery today, 7(2), 143-7. https://doi.org/10.1016/s1359-6446(01)02090-6
Weber, Lutz. "Multi-component reactions and evolutionary chemistry." Drug discovery today vol. 7,2 (2002): 143-7. doi: https://doi.org/10.1016/s1359-6446(01)02090-6
Weber L. Multi-component reactions and evolutionary chemistry. Drug Discov Today. 2002 Jan 15;7(2):143-7. doi: 10.1016/s1359-6446(01)02090-6. PMID: 11790626.
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Showing 1 to 12 of 125 entries