[HTML][HTML] Computational disease modeling–fact or fiction?
Background Biomedical research is changing due to the rapid accumulation of experimental
data at an unprecedented scale, revealing increasing degrees of complexity of biological …
data at an unprecedented scale, revealing increasing degrees of complexity of biological …
Executable cancer models: successes and challenges
Making decisions on how best to treat cancer patients requires the integration of different
data sets, including genomic profiles, tumour histopathology, radiological images, proteomic …
data sets, including genomic profiles, tumour histopathology, radiological images, proteomic …
Agent-based methods facilitate integrative science in cancer
In this opinion, we highlight agent-based modeling as a key tool for exploration of cell–cell
and cell–environment interactions that drive cancer progression, therapeutic resistance, and …
and cell–environment interactions that drive cancer progression, therapeutic resistance, and …
[HTML][HTML] A review of mechanistic learning in mathematical oncology
Mechanistic learning refers to the synergistic combination of mechanistic mathematical
modeling and data-driven machine or deep learning. This emerging field finds increasing …
modeling and data-driven machine or deep learning. This emerging field finds increasing …
Mechanistic models versus machine learning, a fight worth fighting for the biological community?
Ninety per cent of the world's data have been generated in the last 5 years (Machine
learning: the power and promise of computers that learn by example. Report no. DES4702 …
learning: the power and promise of computers that learn by example. Report no. DES4702 …
Modeling and model simplification to facilitate biological insights and predictions
O Eriksson, J Tegnér - Uncertainty in Biology: A Computational Modeling …, 2016 - Springer
Mathematical dynamical models of intracellular signaling networks are continuously
increasing in size and model complexity due in large part to the data explosion in biology …
increasing in size and model complexity due in large part to the data explosion in biology …
Multi-scale modeling in clinical oncology: opportunities and barriers to success
Hierarchical processes spanning several orders of magnitude of both space and time
underlie nearly all cancers. Multi-scale statistical, mathematical, and computational …
underlie nearly all cancers. Multi-scale statistical, mathematical, and computational …
Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset
V Belcastro, C Poussin, S Gebel… - … and biology insights, 2013 - journals.sagepub.com
We recently constructed a computable cell proliferation network (CPN) model focused on
lung tissue to unravel complex biological processes and their exposure-related …
lung tissue to unravel complex biological processes and their exposure-related …
[HTML][HTML] Cell fate forecasting: a data-assimilation approach to predict epithelial-mesenchymal transition
Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a
central role in embryonic development, tissue regeneration, and cancer metastasis …
central role in embryonic development, tissue regeneration, and cancer metastasis …
[HTML][HTML] Learning stochastic process-based models of dynamical systems from knowledge and data
Background Identifying a proper model structure, using methods that address both structural
and parameter uncertainty, is a crucial problem within the systems approach to biology. And …
and parameter uncertainty, is a crucial problem within the systems approach to biology. And …