Results 151 to 160 of about 161,037 (263)

HMLA: A hybrid machine learning approach for enhancing stroke prediction models with missing data imputation techniques. [PDF]

Sci Rep
Singh MS, Thongam K, Kumar K, Choudhary P.   +3 more
europepmc   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

Clustering-Informed Shared-Structure Variational Autoencoder for Missing Data Imputation in Large-Scale Healthcare Data. [PDF]

Stat Med
Khadem Charvadeh Y, Seier K, Panageas KS, Vaithilingam D, Gönen M, Chen Y.   +5 more
europepmc   +1 more source

BraMARS: An Interpretable Histopathology‐Driven Deep Learning Model for Brain Metastasis Risk Stratification in Surgically Resected Limited‐Stage SCLC

Advanced Science, EarlyView.
This study presents BraMARS, an explainable deep learning model that estimates future brain metastasis risk in surgically resected limited‐stage small‐cell lung cancer using routine H&E‐stained whole‐slide images. By linking model‐attributed spatial histopathology with clinical outcomes and proteomic programs, BraMARS provides a biologically ...
Zijian Yang, Taolue Wang, Shilong Liu, Zicheng Zhang, Yibo Zhang, Fan Yang, Bo Yu, Shuaishuai Gao, Yu Chen, Lin Yang, Meng Zhou   +10 more
wiley   +1 more source

Combining Missing Data Imputation and Internal Validation in Clinical Risk Prediction Models. [PDF]

Stat Med
Mi J, Tendulkar RD, Sittenfeld SMC, Patil S, Zabor EC.   +4 more
europepmc   +1 more source

Livestock Multi‐Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation

Advanced Science, EarlyView.
A three‐tier livestock multi‐omics framework resolves four typical analytical pitfalls. Moving from statistical association through machine learning preprocessing to triple‐modal causal inference, it converts omics results into genomic selection and gene editing strategies to achieve One Health, underpinned by multi‐omics data, multimodal sequencing ...
Jiying Wen, Zhongyu Wang, Jieping Huang, Fen Li, Ningbo Chen, Yun Ma   +5 more
wiley   +1 more source

Biomimetic model for computing missing data imputation and inconsistency reduction in pairwise comparisons matrices. [PDF]

PLoS One
Koczkodaj WW, Pedrycz W, Pigazzini A, Pigazzini LP.   +3 more
europepmc   +1 more source

Artifact rejection and missing data imputation in cerebral blood flow velocity signals via trace norm minimization. [PDF]

Physiol Meas, 2020
Allan Gunn C, Hu X, Vandenberghe L.
europepmc   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Bridging the Gap: Missing Data Imputation Methods and Their Effect on Dementia Classification Performance. [PDF]

Brain Sci
Aracri F, Bianco MG, Quattrone A, Sarica A.   +3 more
europepmc   +1 more source