Results 81 to 90 of about 1,915,617 (250)

Reliable causal discovery in time series [PDF]

, 2022
In this talk I will, first, introduce the so-called causal discovery task, that is, the task of learning cause-and-effect relationships from observational data.
Gerhardus, Andreas
core   +1 more source

Liver organoids: modelling complexity in homeostasis and disease

FEBS Letters, EarlyView.
Studying liver in vitro has been challenging because simple 2D cell cultures fail to capture liver's cellular and architectural complexity. To bridge this gap, scientists increasingly use organoids, 3D liver models which better mimic liver composition and function. This review examines recent advances in liver organoid complexity and realism, discusses
Anna M. Dowbaj, Meritxell Huch
wiley   +1 more source

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery

IEEE Open Journal of Signal Processing
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of “independence” to justify the inclusion
Daniel Waxman, Kurt Butler, Petar M. Djuric   +2 more
doaj   +1 more source

Autophagy and mitophagy in pancreatic β‐cell homeostasis and their involvement in diabetes pathophysiology

FEBS Letters, EarlyView.
This review focuses on the role of autophagy and mitophagy in maintaining pancreatic β‐cell function and homeostasis. We discuss how genetic defects affecting these pathways contribute to the development of type 1, type 2, monogenic, and gestational diabetes. We further explore their potential as therapeutic targets. Created in BioRender.
Yunkyeong Lee, Mingming Tong, Anna L. Gloyn   +2 more
wiley   +1 more source

Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry

FEBS Letters, EarlyView.
EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri, Ayaka Nagasato‐Ichikawa, Keita Iida, Shuhei Kimura, Akira Imamoto, Mariko Okada   +5 more
wiley   +1 more source

hyperparams-causal-discovery

<p>The code that implements an extensive testing environtment for causal structure learning algorithms. It is based on an existing framework "Benchpress", further modified here to analyse algorithms' robustness to hyperparameter choice. Accompanies
Samothrakis, Spyridon, Clarke, Paul, Machlanski, Damian   +2 more
core   +1 more source

The Shewanella oneidensis Fic enzyme SoFic targets the switch‐I region of EF‐Tu for AMPylation

FEBS Letters, EarlyView.
Fic enzymes mediate diverse post‐translational modifications across all domains of life, including AMPylation. Prokaryotic EF‐Tu can be AMPylated and deAMPylated by the conserved Fic enzyme SoFic. Structural and biochemical approaches were used to characterize the effect of AMPylation on EF‐Tu, SoFic's enzymatic activities, and the enzyme‐target ...
Svenja Runge, Vivian Pogenberg, Alexander Baumgart, Bente Siebels, Hartmut Schlüter, Michael Hecht‐Bucher, Aymelt Itzen   +6 more
wiley   +1 more source

A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections – CORRIGENDUM

Environmental Data Science, 2023
Xavier-Andoni Tibau, Christian Reimers, Andreas Gerhardus, Joachim Denzler, Veronika Eyring, Jakob Runge   +5 more
doaj   +1 more source

Foundations of causal discovery on groups of variables

Journal of Causal Inference
Discovering causal relationships from observational data is a challenging task that relies on assumptions connecting statistical quantities to graphical or algebraic causal models.
Wahl Jonas, Ninad Urmi, Runge Jakob
doaj   +1 more source

Enhanced Causal Discovery for Autocorrelated Time Series via Adaptive Momentary Conditional Independence

Mathematics
Discovering causal relationships from time series data is essential for understanding complex dynamical systems across a range of domains. However, strong autocorrelation often limits the detection power of existing algorithms and increases the risk of ...
Minglong Gao, Yingchun Zhou
doaj   +1 more source