Hyperlink DOIs to preferred resolver

deps/AMICI/ThirdParty/sundials/README.md

@@ -114,7 +114,7 @@ reporting work done with SUNDIALS:
 * Alan C. Hindmarsh, Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu
 Serban, Dan E. Shumaker, and Carol S. Woodward. 2005. SUNDIALS: Suite of
 nonlinear and differential/algebraic equation solvers. ACM Trans. Math. Softw.
-31, 3 (September 2005), 363-396. DOI=http://dx.doi.org/10.1145/1089014.1089020
+31, 3 (September 2005), 363-396. DOI=https://doi.org/10.1145/1089014.1089020
 
 ## License ##
 SUNDIALS is released under the BSD 3-clause license. See the [LICENSE](./LICENSE)

deps/AMICI/documentation/amici_refs.bib

@@ -300,7 +300,7 @@ @article{LoosMoe2018
 	Title = {A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-To-Cell Variability},
 	Volume = {6},
 	Year = {2018},
-	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.cels.2018.04.008}}
+	Bdsk-Url-1 = {https://doi.org/10.1016/j.cels.2018.04.008}}
 
 @article{MaierLoo2017,
 	Author = {Maier, C. and Loos, C. and Hasenauer, J.},
@@ -457,7 +457,7 @@ @Article{SchmiesterSch2019
   year     = {2019},
   month    = {07},
   issn     = {1367-4803},
-  abstract = {{Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset, and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (\\>1000 state variables, \\>4000 parameters) using relative protein, phospho-protein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements.Supplementary information are available at Bioinformatics online. Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441.}},
+  abstract = {{Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset, and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (\\>1000 state variables, \\>4000 parameters) using relative protein, phospho-protein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements.Supplementary information are available at Bioinformatics online. Supplementary code and data are available online at https://doi.org/10.5281/zenodo.3254429 and https://doi.org/10.5281/zenodo.3254441.}},
   doi      = {10.1093/bioinformatics/btz581},
   eprint   = {http://oup.prod.sis.lan/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz581/29004243/btz581.pdf},
   url      = {https://doi.org/10.1093/bioinformatics/btz581},