| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Editor’s note on the themed issue: integration of machine learning and quantitative systems pharmacology
|
Bonate, Peter L.
|
|
|
49 |
1 |
p. 1-3
|
artikel
|
| 2 |
From complex data to biological insight: ‘DEKER’ feature selection and network inference
|
Hayes, Sean M. S.
|
|
|
49 |
1 |
p. 81-99
|
artikel
|
| 3 |
From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building
|
Putnins, M.
|
|
|
49 |
1 |
p. 101-115
|
artikel
|
| 4 |
Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action
|
Parikh, Jaimit
|
|
|
49 |
1 |
p. 51-64
|
artikel
|
| 5 |
Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression
|
McComb, Mason
|
|
|
49 |
1 |
p. 65-79
|
artikel
|
| 6 |
Recent applications of quantitative systems pharmacology and machine learning models across diseases
|
Aghamiri, Sara Sadat
|
|
|
49 |
1 |
p. 19-37
|
artikel
|
| 7 |
Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure
|
Cheng, Limei
|
|
|
49 |
1 |
p. 39-50
|
artikel
|
| 8 |
Thanks to our reviewers 2021!
|
|
|
|
49 |
1 |
p. 133-134
|
artikel
|
| 9 |
Two heads are better than one: current landscape of integrating QSP and machine learning
|
Zhang, Tongli
|
|
|
49 |
1 |
p. 5-18
|
artikel
|
| 10 |
Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modelling
|
Zhang, Tongli
|
|
|
49 |
1 |
p. 117-131
|
artikel
|
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