Results 21 to 30 of about 26,939 (224)
Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, dynamic inference of an evolving cell population from time series scRNA-seq data is challenging owing to the stochasticity and nonlinearity of the underlying biological ...
Qi Jiang, Shuo Zhang, Lin Wan
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What can causal networks tell us about metabolic pathways? [PDF]
Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time.
Rachael Hageman Blair +2 more
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Hyperbolastic Models from a Stochastic Differential Equation Point of View
A joint and unified vision of stochastic diffusion models associated with the family of hyperbolastic curves is presented. The motivation behind this approach stems from the fact that all hyperbolastic curves verify a linear differential equation of the ...
Antonio Barrera +2 more
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REHEATFUNQ (REgional HEAT-Flow Uncertainty and aNomaly Quantification) 2.0.1: a model for regional aggregate heat flow distributions and anomaly quantification [PDF]
Surface heat flow is a geophysical variable that is affected by a complex combination of various heat generation and transport processes. The processes act on different lengths scales, from tens of meters to hundreds of kilometers.
M. J. Ziebarth +2 more
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Towards a Stochastic Paradigm: From Fuzzy Ensembles to Cellular Functions
The deterministic sequence → structure → function relationship is not applicable to describe how proteins dynamically adapt to different cellular conditions. A stochastic model is required to capture functional promiscuity, redundant sequence
Monika Fuxreiter
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Inference from gated first-passage times
First-passage times provide invaluable insight into fundamental properties of stochastic processes. Yet, various forms of gating mask first-passage times and differentiate them from actual detection times.
Aanjaneya Kumar +3 more
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Efficient representation of boolean decision structures through Boolean function optimization
A binary decision tree (BDT) is stochastic and depth-dependent when inference is performed. The lower and upper bounds are derived from the minimum and maximum heights of the leaf nodes.
Maddimsetti Srinivas, Debdoot Sheet
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Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the ...
Anđela Davidović +3 more
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The effective reproduction number (ℜt) is a theoretical indicator of the course of an infectious disease that allows policymakers to evaluate whether current or previous control efforts have been successful or whether additional interventions are ...
Jair Andrade, Jim Duggan
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Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive ...
Xiang Ge Luo +2 more
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