Results 151 to 160 of about 178,240 (253)

Assessing Genotype Imputation Methods for Low-Coverage Sequencing Data in Populations With Differing Relatedness and Inbreeding Levels. [PDF]

Mol Ecol Resour
Vi T, Stuart KC, Tan HZ, Lloret-Villas A, Santure AW.   +4 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

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

Imputation methods for missing failure times in recurrent-event survival analysis: Application to suicide attempts in the transgender population. [PDF]

PLoS One, 2022
Liu S, Reisner SL, Herman JL, Weller E.
europepmc   +1 more source

The impact of missing data rates and imputation methods on the assumption of unidimensionality. [PDF]

PLoS One
Baniamer AO.
europepmc   +1 more source

scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods. [PDF]

Nucleic Acids Res, 2022
Dai C, Jiang Y, Yin C, Su R, Zeng X, Zou Q, Nakai K, Wei L.   +7 more
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

Are dropout imputation methods for scRNA-seq effective for scHi-C data? [PDF]

Brief Bioinform, 2021
Han C, Xie Q, Lin S.
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source