Results 151 to 160 of about 2,218,762 (336)

Fabricating Microfluidic Co‐Cultures of Immortalized Cell Lines Uncovers Robust Design Principles for the Simultaneous Formation of Patterned, Vascularized, and Stem Cell‐Derived Adipose Tissue

Small, EarlyView.
Murphy et al present an optimized co‐culture protocol for immortalized cell line vasculogenesis. Here, immortalized co‐cultures form reproducible microvessel networks across 31‐days, while gradient microchips enable simultaneous mesenchymal stem cell (MSC) adipogenesis and endothelial cell (EC) vasculogenesis.
Ashley R. Murphy, Rose Ann Franco, Mark C. Allenby   +2 more
wiley   +1 more source

Sparse Matrix Factorizations for Fast Linear Solvers with Application to Laplacian Systems [PDF]

, 2017
Michael T. Schaub, Maguy Tréfois, Paul Van Dooren, Jean‐Charles Delvenne   +3 more
openalex   +1 more source

POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi‐Omics Data with Machine Learning

Small Methods, EarlyView.
Long non‐coding RNAs (lncRNAs) are receiving increasing attention as biomarkers for cancer diagnosis and therapy, highlighting the urgent need for computational methods to accelerate their comprehensive discovery. Here, to better predict and provide functional insight into cancer lncRNAs, a novel interpretable machine‐learning method (POCALI) is ...
Ziyan Rao, Chenyang Wu, Yunxi Liao, Chuan Ye, Shaodong Huang, Dongyu Zhao   +5 more
wiley   +1 more source

On spectrum and energies of enhanced power graphs

Mathematics Open
The enhanced power graph [Formula: see text] of a group G is a simple graph with vertex set G and two distinct vertex are adjacent if and only if they belong to the same cyclic subgroup.
Pankaj Kalita, Prohelika Das
doaj   +1 more source

Thermal Imaging for Quality Control in Thin Silicon‐Based Coatings for Lithium‐Ion Batteries: Defect Detection, Drying Dynamics, and Machine Learning‐Based Mass Loading Estimation

Small Methods, EarlyView.
Thermal imaging offers a non‐destructive approach to quality control in silicon‐based lithium‐ion battery electrodes, enabling the detection of defects, variations in mass loading, and the monitoring of drying dynamics. This study introduces an automated defect‐detection‐algorithm and a machine learning‐based Random Forest model to estimate mass ...
Adil Amin, Philipp Valentin Geiping, Ahammed Suhail Odungat, Fatih Özcan, Doris Segets   +4 more
wiley   +1 more source

The nullity of the net Laplacian matrix of a signed graph

American Journal of Combinatorics
The net Laplacian matrix of a signed graph \(\Gamma = (G, \sigma)\), where \(G = (V(G),E(G))\) is an unsigned graph (referred to as the underlying graph) and \(\sigma: E(G) \rightarrow \{-1, +1\}\) is the sign function, is defined as \(L^{\pm}(\Gamma) = D^{\pm}(\Gamma) - A(\Gamma)\).
openaire   +3 more sources

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang, Jian Yang, Bob Zhang, Yang Tang, Wenli Du, Bihan Wen   +5 more
wiley   +1 more source

Boosted unsupervised feature selection for tumor gene expression profiles

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 more
wiley   +1 more source

Multi-label feature selection based on dynamic graph Laplacian

Tongxin xuebao, 2020
In view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix, as well as such methods employ logical-value labels to guide feature selection process and loses label information, a ...
Yonghao LI, Liang HU, Ping ZHANG, Wanfu GAO   +3 more
doaj   +2 more sources