Interpretation of Machine Learning Models for Data Sets with Many Features Using Feature Importance. [PDF]
ACS Omega, 2023 Kaneko H.
europepmc +1 more source
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
Semi-supervised data-integrated feature importance enhances performance and interpretability of biological classification tasks. [PDF]
BioinformaticsKim JW, Altman RB.
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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
Multi-sensor observer-based residual learning with Auto-Permutation Feature Importance for fault diagnosis of multistage centrifugal pumps under variable pressures. [PDF]
Sci RepUllah S, Siddique MF, Kim JM.
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Advancing aircraft engine RUL predictions: an interpretable integrated approach of feature engineering and aggregated feature importance. [PDF]
Sci Rep, 2023 Alomari Y, Andó M, Baptista ML.
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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
A DEEP NEURAL NETWORK TWO-PART MODEL AND FEATURE IMPORTANCE TEST FOR SEMI-CONTINUOUS DATA. [PDF]
Ann Appl StatZou B +6 more
europepmc +1 more source
Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population. [PDF]
PLoS One, 2023 Huang AA, Huang SY.
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