Poster
Statistical approaches for the analysis of RNA-Seq and ChIP-seq data and their integration |
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Brain functional connectivity at rest as similarity of neuronal activities |
The brain is a connected network, requiring complex-system measures to describe its organization principles. The normalized compression distance (NCD) [1] is a parameter -free, quasi universal similarity measure that estimates the information shared by two signals comparing the compression length… | |
A Machine Learning Approach for Disease Genes Signatures |
In the context of network medicine, disease genes, i.e. genes that have been experimentally associated to the onset or progression of a pathology, show a complex set of features that are not easily reduced to, and grasped by a simple network approach (e.g., studying centrality measures or… | |
Kinetics of in vivo proliferation and death of memory and naive CD8 T cells: parameter estimation based on BrdU incorporation in spleen, lymph nodes and bone marrow. |
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Dynamic simulation of a flexible transport system |
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Welcome to NeuroPype: A Python-based pipeline for advanced MEG and EEG connectivity analyses |
With the exponential increase in data dimension and methodological complexities, conducting brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. To date, most of the MEG/EEG processing is done by combining software packages and custom tools… | |
The storage capacity of an associative memory with superimposed traces |
No abstract available | |
Peer Training in Applied Scientific Computing |
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Time-course whole-genome microarray analysis of estrogen effects on hormone-responsive breast cancer cells |
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A hierarchical Krylov-Bayes iterative inverse solver for MEG with anatomical prior |
In the present study, we revisit the MEG inverse problem, regularization and depth weighting from a Bayesian hierarchical point of view: the primary unknown is the discretized current density and each dipole has a preferred direction extracted from the MRI of the subject and encoded in the prior… |
