Results 231 to 240 of about 287,840 (285)

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding. [PDF]

Brief Bioinform
Gao Z, Su Y, Xia J, Cao RF, Ding Y, Zheng CH, Wei PJ.   +6 more
europepmc   +1 more source

Pattern Formation in Non‐Equilibrium Architected Materials

Advanced Materials Technologies, EarlyView.
This article demonstrates an artificial mechanical system ‐ a robotic metamaterial ‐ as an accessible and versatile platform within which to explore and prescribe the reaction‐diffusion driven pattern formation hitherto associated with comparatively less accessible and versatile non‐equilibrium biological and chemical systems.
Vinod Ramakrishnan, Michael J. Frazier
wiley   +1 more source

A knowledge graph embedding model based attention mechanism for enhanced node information integration. [PDF]

PeerJ Comput Sci
Liu Y, Wang P, Yang D, Qiu N.
europepmc   +1 more source

3D Printed Multimaterial Microfluidic Transistors

Advanced Materials Technologies, EarlyView.
We introduce a biocompatible, high resolution photopolymer resin that closely mimics the Young's Modulus (elasticity) and reversible stretchability (no hysteresis) of poly(dimethylsiloxane) (PDMS), enabling the fabrication of microfluidic transistors (i.e., microvalves capable of proportional amplification) by multimaterial stereolithography (mSLA ...
Alireza Ahmadianyazdi, Wei Huang, Kaustav A. Gopinathan, Ivan Stepanov, Lisa Horowitz, Ting‐Yuan Tu, Mehmet Toner, Albert Folch   +7 more
wiley   +1 more source

Gold Nanorods in Cardiac Millitissues: Assessing Mechanical and Contractile Dynamics in a Living Engineering Myocardium Model

Advanced Materials Technologies, EarlyView.
This study presents a novel platform for assessing the active mechanical behavior of living cardiac microbundles through localized nanoindentation, integrated with temperature regulation and dual‐camera imaging systems. The developed system enables quantitative evaluation of dynamic micromechanics in engineered cardiac tissues in vitro, offering ...
Lihua Lou, Lia Paolino, Alberto Sesena Rubfiaro, Jin He, Arvind Agarwal   +4 more
wiley   +1 more source

scBiG for representation learning of single-cell gene expression data based on bipartite graph embedding. [PDF]

NAR Genom Bioinform
Li T, Qian K, Wang X, Li WV, Li H.
europepmc   +1 more source

Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection. [PDF]

Bioinformatics
Liu E, Lyu H, Liu Y, Fu L, Cheng X, Yin X.   +5 more
europepmc   +1 more source

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Unsupervised Graph Embedding via Adaptive Graph Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed.
Rui Zhang, Yunxing Zhang, Chengjun Lu, Xuelong Li   +3 more
openaire   +4 more sources

Graph-Embedded Lane Detection

IEEE Transactions on Image Processing, 2021
Lane detection on road segments with complex topologies such as lane merge/split and highway ramps is not yet a solved problem. This paper presents a novel graph-embedded solution. It consists of two key parts, a learning-based low-level lane feature extraction algorithm, and a graph-embedded lane inference algorithm.
Pingping Lu, Shaobing Xu, Huei Peng
openaire   +2 more sources