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Found 12 papers, 7 papers with code
Exploring the impact of social stress on the adaptive dynamics of COVID-19: Typing the behavior of naïve populations faced with epidemics
In the context of natural disasters, human responses inevitably intertwine with natural factors.
Domain Adaptation Principal Component Analysis: base linear method for learning with out-of-distribution data
Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain).
Computational challenges of cell cycle analysis using single cell transcriptomics
One of the major difficulties for existing packages is distinguishing between two patterns of cell cycle transcriptomic dynamics: normal and characteristic for embryonic stem cells (ESC), with the latter one shared by many cancer cell lines.
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation
Recent work by Mellor et al (2021) showed that there may exist correlations between the accuracies of trained networks and the values of some easily computable measures defined on randomly initialised networks which may enable to search tens of thousands of neural architectures without training.
Scikit-dimension: a Python package for intrinsic dimension estimation
Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID).
Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data
Large observational clinical datasets become increasingly available for mining associations between various disease traits and administered therapy.
Local intrinsic dimensionality estimators based on concentration of measure
In this paper, we introduce new local estimators of ID based on linear separability of multi-dimensional data point clouds, which is one of the manifestations of concentration of measure.
Synthesis of Boolean Networks from Biological Dynamical Constraints using Answer-Set Programming
Boolean networks model finite discrete dynamical systems with complex behaviours.
Estimating the effective dimension of large biological datasets using Fisher separability analysis
Modern large-scale datasets are frequently said to be high-dimensional.
Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph
Large datasets represented by multidimensional data point clouds often possess non-trivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples.
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0
This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios.
Basic and simple mathematical model of coupled transcription, translation and degradation
Synthesis of proteins is one of the most fundamental biological processes, which consumes a significant amount of cellular resources.
Molecular Networks
