Results 101 to 110 of about 88,237 (314)

Yy-New/NDW-KNN: First release of NDW-KNN

<p>This is the first release.</p ...
Yy
core   +1 more source

Single‐Cell Annotation and Localization via Integrating Spatial Transcriptomics Maps the Mouse Ocular Atlas and RAO Dynamics

Advanced Science, EarlyView.
We developed the ASCAL pipeline, integrating complementary spatial transcriptomics, to construct a high‐fidelity mouse whole‐eye single‐cell atlas. Applying ASCAL to a retinal artery occlusion (RAO) model revealed spatially restricted immune activation localized to the ganglion cell layer and the selective depletion of a translationally active, outer ...
Chen Du, Yinming Li, Ziyue Li, Yuedan Wang, Shaopeng Li, Yijie Wang, Yingying Qu, Bingyang Lv, Ying Li, Ting Chen, Yu Zhou, Xuan Xiao   +11 more
wiley   +1 more source

Double-weighted kNN: a simple and efficient variant with embedded feature selection

Predictive modeling aims at providing estimates of an unknown variable, the target, from a set of known ones, the input. The k Nearest Neighbors (kNN) is one of the best-known predictive algorithms due to its simplicity and well behavior.
Moreno-Ribera, Almudena, Calviño Martínez, Aída   +1 more
core   +1 more source

A Periosteum‐Inspired Janus Piezoelectric Scaffold Using Bioenergetic‐Driven H‐Type Vascularization for Diabetic Bone Regeneration

Advanced Science, EarlyView.
A periosteum‐inspired Janus piezoelectric scaffold converts micromechanical forces into electrical signals and synergizes with controlled resveratrol release. This dual action reprograms endothelial metabolism, drives H‑type vessel formation, and restores bone–vessel coupling in diabetic bone defects.
Kai Wang, Kai Jiang
wiley   +1 more source

Accurately Deciphering Tissue Heterogeneity From Spatial Multi‐Modal and Multi‐Omics With STransformer

Advanced Science, EarlyView.
STransformer is a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short‐range cellular interactions and tissue‐wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity.
Xingyi Li, Jialuo Xu, Gaoyuan Du, Xiangting Jia, Dongmin Zhao, Chunyan Zhou, Kexin Xiao, Jia Gu, Junnan Zhu, Xuequn Shang   +9 more
wiley   +1 more source

Impressively fast and efficient KNN construction

, 2015
K-Nearest-Neighbor (KNN) graphs have emerged as a fundamentalbuilding block of many on-line services such as recommendation,similarity search and classification.
Boutet, Antoine, Mittal, Nupur, Taïani, François, Kermarrec, Anne-Marie   +3 more
core   +1 more source

Comparison of the Performance of Random Forest and K-Nearest Neighbor in Classifying Leukemia Using Principal Component Analysis

Jurnal Sisfokom
Leukemia is the most common blood cancer in Asia, one of which is Indonesia. Leukemia can affect blood cells, bone marrow, lymph nodes and other parts of the lymphatic system.
Sriani Sriani, Muhammad Ikhsan, lailan sofinah harahap   +2 more
doaj   +1 more source

Machine Learning‐Enhanced Ultrasensitive Immuno‐CRISPR Array Facilitates Early Diagnosis of Alzheimer's Disease by Detecting Multiple Plasma Biomarkers

Advanced Science, EarlyView.
This work presents a CRISPR‐based, ultrasensitive multiplex protein detection array capable of simultaneously analyzing six plasma biomarkers associated with Alzheimer's disease (AD). By integrating antibody‐mediated signal transduction, recombinase polymerase amplification, and spatially encoded CRISPR‐Cas12a, the system achieves detection sensitivity
Liding Zhang, Changwen Yang, Qian Yao, Xuewei Du, Shuai Ding, Yaoqiang Shi, Can Sheng, Ming Wang, Ying Han, Haiming Luo   +9 more
wiley   +1 more source

A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection [PDF]

, 2015
This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based ...
Suwimol Jungjit, Alex Freitas, Jungjit, Suwimol, Freitas, Alex A.   +3 more
core   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source