Results 161 to 170 of about 23,976 (198)

Torque Teno Virus or Herpesviruses Detection By Metagenomic Next-Generation Sequencing Predicts In-Hospital Major Adverse Events in Critically Ill Patients With Severe Infections. [PDF]

J Med Virol
Sun Y, Shuai X, Sheng Q, Lu Y, Wu Z, Sun Y, Wu D, Guo X.   +7 more
europepmc   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Sensitive listening of women-mothers caring for children with cancer about self-care. [PDF]

Rev Bras Enferm
Formigosa DEC, Carvalho GP, Formigosa LAC, de Carvalho HNM, Formigosa JDC, Teixeira RDC, Nascimento MHM, Ferreira MGS.   +7 more
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Evaluating cross-dataset transfer learning for photoplethysmography-based blood pressure estimation. [PDF]

Sci Rep
Kim YC, Baek HJ.
europepmc   +1 more source

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

Advanced Intelligent Discovery, EarlyView.
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen, Christina Ertural, Philipp Benner, Gian‐Marco Rignanese, Janine George   +6 more
wiley   +1 more source

A propolis-rich hive environment affects redox gene expression and gut microbiota at the individual and social level in honey bees. [PDF]

FEMS Microbes
Anderson KE, Copeland DC, Mott BM, Kortenkamp OL, Erickson RJ, Allen NO, Maes PW, Chao N, Dalenberg H, Spivak M.   +9 more
europepmc   +1 more source