@@ -16,7 +16,7 @@ optimization problems requiring up to thousands of simulations per objective
1616function evaluation on high performance computing (HPC) systems.
1717
1818parPE offers easy integration with
19- [ AMICI] ( https://github.com/ICB-DCM /AMICI ) -generated ordinary differential
19+ [ AMICI] ( https://github.com/AMICI-dev /AMICI ) -generated ordinary differential
2020equation (ODE) models.
2121
2222## Features
@@ -27,9 +27,9 @@ parPE offers the following features:
2727* improved load balancing by intermingling multiple optimization runs
2828 (multi-start local optimization)
2929* simple integration with [ SBML] ( http://sbml.org/ ) models via
30- [ AMICI] ( https://github.com/ICB-DCM /AMICI ) and
30+ [ AMICI] ( https://github.com/AMICI-dev /AMICI ) and
3131 [ PEtab] ( https://github.com/PEtab-dev/PEtab )
32- * interfaces to [ Ipopt] ( http ://www.coin-or.org/Ipopt/) ,
32+ * interfaces to [ Ipopt] ( https ://www.coin-or.org/Ipopt/) ,
3333 [ Ceres] ( http://ceres-solver.org/ ) ,
3434 [ FFSQP] ( https://www.isr.umd.edu/news/news_story.php?id=4088 ) and
3535 [ SUMSL (CALGO/TOMS 611)] ( http://www.netlib.org/toms/index.html ) optimizers
@@ -41,7 +41,7 @@ parPE offers the following features:
4141
4242Although various modules of parPE can be used independently, the most
4343meaningful and convenient use case is parameter optimization for an SBML model
44- specified in the [ PEtab] ( https://github.com/ICB-DCM /PEtab ) format. This is
44+ specified in the [ PEtab] ( https://github.com/PEtab-dev /PEtab ) format. This is
4545described in [ doc/petab_model_import.md] ( doc/petab_model_import.md ) .
4646
4747## Dependencies
@@ -58,7 +58,7 @@ For full functionality, parPE requires the following libraries:
5858* [ Boost] ( https://www.boost.org/ ) (serialization, thread)
5959* HDF5 (>= 1.10)
6060* CBLAS compatible BLAS (libcblas, Intel MKL, ...)
61- * [ AMICI] ( https://github.com/ICB-DCM /AMICI ) (included in this repository)
61+ * [ AMICI] ( https://github.com/AMICI-dev /AMICI ) (included in this repository)
6262 (uses SuiteSparse, Sundials)
6363* C++17 compiler
6464* Python >= 3.7, including header files
@@ -136,11 +136,10 @@ parPE is being used or has been used in the following projects:
136136 Bioinformatics, btz581, [ doi:10.1093/bioinformatics/btz581] ( https://doi.org/10.1093/bioinformatics/btz581 )
137137 (preprint: [ doi:10.1101/579045] ( https://www.biorxiv.org/content/10.1101/579045v1 ) ).
138138
139- - Paul Stapor, Leonard Schmiester, Christoph Wierling, Bodo Lange,
140- Daniel Weindl, and Jan Hasenauer. 2019.
141- * Mini-Batch Optimization Enables Training of Ode Models on Large-Scale Datasets.*
142- bioRxiv. Cold Spring Harbor Laboratory.
143- preprint: [ doi:10.1101/859884] ( https://doi.org/10.1101/859884 ) .
139+ - Stapor, P., Schmiester, L., Wierling, C. et al. * Mini-batch optimization*
140+ * enables training of ODE models on large-scale datasets* . Nat Commun 13, 34
141+ (2022). [ doi:10.1038/s41467-021-27374-6] ( https://doi.org/10.1038/s41467-021-27374-6 )
142+ (preprint: [ doi:10.1101/859884] ( https://doi.org/10.1101/859884 ) ).
144143
145144- [ CanPathPro] ( http://canpathpro.eu/ )
146145
@@ -155,4 +154,4 @@ parPE has been developed within research projects receiving external funding:
155154
156155* Computer resources for testing parPE have been provided among others by the
157156 Gauss Centre for Supercomputing / Leibniz Supercomputing Centre under grant
158- pr62li.
157+ pr62li and pn72go .
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