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FEBS Open Bio, EarlyView.We found that during chemotherapy‐induced alopecia (CIA), Sonic hedgehog (Shh) expression significantly decreased in hair follicle Shh+ cells, whereas the Janus‐activated kinase/signal transducer and activator of transcription 1 (JAK/STAT1) signaling pathway was markedly activated.
Ruifang Fan +6 more
wiley +1 more source
Raman‐based label‐free microscopic analysis of the pancreas in living zebrafish larvae
FEBS Open Bio, EarlyView.Forward stimulated Raman scattering (F‐SRS) and epi coherent anti‐Stokes Raman scattering (E‐CARS) allow label‐free discrimination of distinct subcellular structures in the pancreas of living zebrafish larvae. Given the straightforward applicability, we anticipate broad implementation of Raman microscopy in other organs and across various biomedical ...
Noura Faraj +3 more
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arXiv.orgCausal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains.
Ehsan Mokhtarian +3 more
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Argumentative Causal Discovery
Proceedings of the TwentyFirst International Conference on Principles of Knowledge Representation and ReasoningCausal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this
Fabrizio Russo +2 more
semanticscholar +3 more sources
Stable Differentiable Causal Discovery
International Conference on Machine Learning, 2023 Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this problem, framing the search as a continuous optimization.
Achille Nazaret +3 more
semanticscholar +3 more sources
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Causal Discovery via Causal Star Graphs
ACM Transactions on Knowledge Discovery from Data, 2023Discovering causal relationships among observed variables is an important research focus in data mining. Existing causal discovery approaches are mainly based on constraint-based methods and functional causal models (FCMs). However, the constraint-based method cannot identify the Markov equivalence class and the functional causal models cannot identify
Boxiang Zhao +5 more
openaire +1 more source
Causal KL: Evaluating Causal Discovery
2022The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model. Both of these metrics maximally reward the true model. However, we argue that they are both insufficiently discriminating in judging the relative merits of ...
O'Donnell, R T, Korb, K B, Allison, L
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Integrating Large Language Model for Improved Causal Discovery
IEEE Transactions on Artificial Intelligence, 2023Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios.
Taiyu Ban +6 more
semanticscholar +1 more source
Causal Discovery Using A Bayesian Local Causal Discovery Algorithm
2004This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the “locality” for
Subramani, Mani, Gregory F, Cooper
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