Results 211 to 220 of about 720,784 (299)

Multi-view graph-regularized deep metric subspace clustering network. [PDF]

PLoS One
Luo P, Yang M, Peng C, Wang Q.
europepmc   +1 more source

Machine Learning Unveils Isolated‐Surrounded Pt Motifs in High‐Entropy Alloys for Superior Low‐Temperature Ammonia Oxidation

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A physics‐informed machine learning approach successfully decodes the complex catalytic activity of high‐entropy alloys for ammonia oxidation. By revealing a synergistic mechanism involving lattice and electronic couplings, the study identifies a superior “isolated‐surrounded” platinum motif.
Shangfeng Jiang, Ting Tao, Kexiang Guo, Xinyu Fan, Xu Zhang, Zhen Zhou   +5 more
wiley   +1 more source

Data-driven modeling of spatiotemporal dynamics using multimodal imaging data. [PDF]

PLoS Comput Biol
Li C, Mao Y, Liu X, Hao W.
europepmc   +1 more source

Searching for the Core of Conspiracy Belief Systems: The Relationship Between Specific Beliefs and Generic Mentality

European Journal of Social Psychology, EarlyView.
ABSTRACT Conspiracism is commonly measured through specific conspiracy beliefs and generalized conspiracy mentality, a broad tendency to suspect conspiracies behind events. Researchers often assume that these measures are psychometrically distinct and that mentality accounts for correlations among beliefs.
Arturo Bertero, Matt Williams, Federico Vegetti, Moreno Mancosu   +3 more
wiley   +1 more source

Unsupervised Feature Selection via Self-Supervised HSIC and Elastic Net Regularization. [PDF]

Entropy (Basel)
Chen Y, Wang T, Zou L.
europepmc   +1 more source

Predicting levetiracetam neuropsychiatric adverse effects using graph‐theoretical electroencephalographic network metrics in drug‐naive focal epilepsy: A multicenter longitudinal study

Epilepsia, EarlyView.
Abstract Objective Levetiracetam (LEV) is an antiseizure medication approved for focal and generalized epilepsy. Despite its efficacy, neuropsychiatric adverse effects (NP‐AEs) may lead to treatment discontinuation. Quantitative electroencephalographic (qEEG) graph‐theoretical analysis has emerged as a valuable tool for identifying neurophysiological ...
Marco Sferruzzi, Lorenzo Ricci, Margherita Anna Grazia Matarrese, Francesca Izzi, Fabio Placidi, Biagio Maria Sancetta, Emanuele Cerulli Irelli, Patrizia Pulitano, Vincenzo Di Lazzaro, Mario Tombini, Giovanni Assenza   +10 more
wiley   +1 more source

DynaTCR: dynamic hard-negative ensemble graph learning improves TCR-epitope binding prediction. [PDF]

Bioinformatics
Fu X, Zhang X, Zhuo L, Chen Y, Cao D, Zou Q.   +5 more
europepmc   +1 more source

Psychopathological and Temperamental Stratification of Eating Disorders With Clustering: From Diagnosis to Phenotypic Diversity

European Eating Disorders Review, EarlyView.
ABSTRACT Objective Eating disorders are highly heterogeneous, challenging diagnostic classification and treatment personalisation. This study aimed to identify data‐driven phenotypes of anorexia nervosa and bulimia nervosa and examine their relationship with DSM‐5 diagnoses and short‐term treatment outcomes. Methods We analysed 809 female patients with
Andrea Zanola, Louis Fabrice Tshimanga, Valentina Meregalli, Angela Favaro, Manfredo Atzori, Enrico Collantoni   +5 more
wiley   +1 more source

Multimodal Brain Graph Analysis for Uncovering Path-Based Biomarkers in Schizophrenia: Integrating Static and Dynamic Approaches. [PDF]

Hum Brain Mapp
Falakshahi H, Rokham H, Mathalon DH, Ford JM, Mueller BA, Preda A, van Erp TGM, Turner JA, Pearlson G, Calhoun VD.   +9 more
europepmc   +1 more source

Addressing the Abundance Paradox Through Topology‐Guided Graph Learning for Cross‐Cohort Classification of Type 2 Diabetes

iMetaMed, EarlyView.
By shifting from highly variable taxonomic abundance to conserved ecological network topologies, our Dual‐Stream Graph Attention Network (DS‐GAT‐CP) resolves the “abundance paradox,” enabling highly accurate and cross‐regionally robust non‐invasive screening for Type 2 Diabetes. ABSTRACT Gut microbiome‐based machine learning models for Type 2 Diabetes (
Changjian Wu, Ping Wei, Xuhui Wang, Hua Hua, Junli Li   +4 more
wiley   +1 more source