Results 81 to 90 of about 36,247 (313)
Hyperosmotic stress induces PARP1‐mediated HPF1‐dependent mono(ADP‐ribosyl)ation
FEBS Letters, EarlyView.Sorbitol‐induced hyperosmotic stress rapidly induces reversible mono(ADP‐ribosyl)ation (MARylation) on PARP1 without the signs of genotoxic signaling. We show that PARP1 autoMARylation is HPF1 dependent and forms hydroxylamine‐resistant O‐glycosidic linkages.
Anna Georgina Kopasz +11 more
wiley +1 more source
Coherentice: Invertible Concept-Based Explainability Framework for CNNs beyond Fidelity
In their natural form, convolutional neural networks (CNNs) lack interpretability despite their effectiveness in visual categorization. Concept activation vectors (CAVs) offer human-interpretable quantitative explainability, utilizing feature maps from ...
Gao, Y, Zhou, J, Akpudo, UE, Lewis, A
core +1 more source
From Robustness to Explainability and Back Again
, 2023 In contrast with ad-hoc methods for eXplainable Artificial Intelligence (XAI), formal explainability offers important guarantees of rigor. However, formal explainability is hindered by poor scalability for some families of classifiers, the most ...
Huang, Xuanxiang, Marques-Silva, Joao
core +1 more source
FEBS Letters, EarlyView.Plasma membranes contain dynamic nanoscale domains that organize lipids and receptors. Because viruses operate at similar scales, this architecture shapes early infection steps, including attachment, receptor engagement, and entry. Using influenza A virus and HIV‐1 as examples, we highlight how receptor nanoclusters, multivalent glycan interactions ...
Jan Schlegel, Christian Sieben
wiley +1 more source
FEBS Letters, EarlyView.Embryo‐like structures (stembryos) are an innovative tool, but they are hindered by experimental variability and limited developmental potential. DNA methylation is crucial for mammalian development, but its status in stembryo models is poorly characterized.
Sara Canil +4 more
wiley +1 more source
Quantifying the Performance of Explainability Algorithms [PDF]
, 2020 Given the complexity of the deep neural network (DNN), DNN has long been criticized for its lack of interpretability in its decision-making process. This 'black box' nature has been preventing the adaption of DNN in life-critical tasks. In recent years,
lin, zhong-qiu
core
IEEE AccessLocal model-agnostic Explainable Artificial Intelligence (XAI), such as LIME or SHAP, has recently gained popularity among researchers and data scientists for explaining black box Machine Learning (ML) models.
Zhaopeng Li +4 more
doaj +1 more source
pH‐mediated activation of the lysosomal arginine sensor SLC38A9
FEBS Letters, EarlyView.Cells monitor nutrient levels via the lysosomal transporter SLC38A9 to activate the mechanistic target of rapamycin complex 1 (mTORC1). This study reveals that SLC38A9 function is regulated by pH. We identified histidine 544 as a critical pH sensor that undergoes conformational changes to control amino acid efflux from lysosomes; therefore, it ...
Xuelang Mu, Ampon Sae Her, Tamir Gonen
wiley +1 more source
Intent-Aware Example-Based Explainability
In this PhD research, I aim to investigate the limitations inherent in current example-based explainability methods, develop solutions to address these deficiencies, and propose a novel direction for research that aligns example-based explainability with
Nematov, Ikhtiyor
core +1 more source
Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
, 2020 Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical machine learning algorithms offer high prediction performance, but most of them lack explanatory power.
Ariza Garzón, Miller Janny +3 more
core +1 more source