Results 151 to 160 of about 449,358 (260)

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

Predictive modeling of gene expression and localization of DNA binding site using deep convolutional neural networks. [PDF]

PLoS Comput Biol
Karshenas A, Röschinger T, Garcia HG.
europepmc   +1 more source

PAIR: Reconstructing Single‐Cell Open‐Chromatin Landscapes for Transcription Factor Regulome Mapping

Advanced Science, EarlyView.
scATAC‐seq analysis is often constrained by limited sequencing depth, extreme sparsity, and pervasive technical missingness. PAIR is a probabilistic framework that restores scATAC‐seq accessibility profiles by directly modeling the native cell–peak bipartite structure of chromatin accessibility.
Yanchi Su, Qi Qi, Yi Fan, Yubo Wang, Gaoyang Hao, Ka‐Chun Wong, Yunhe Wang, Xiangtao Li   +7 more
wiley   +1 more source

Blind Image Quality Assessment Using Convolutional Neural Networks. [PDF]

Sensors (Basel)
Frackiewicz M, Palus H, Trojanowski W.
europepmc   +1 more source

Ferroelectric Devices for In‐Memory and In‐Sensor Computing

Advanced Science, EarlyView.
Inspired by biological systems, in‐memory and in‐sensor computing overcome von Neumann bottlenecks. Ferroelectric devices can mimic synaptic functions and sense stimuli like light or force, therefore are ideal for these paradigms. This review introduces the ferroelectric devices applied for in‐memory and in‐sensor computing, covering their structures ...
Hong Fang, Ronghuan Xie, Qiang Cao, Shishen Yan, Xiaolin Wang, Limei Zheng   +5 more
wiley   +1 more source

Deep Convolutional Neural Networks for Autofocus Control on a <i>C. elegans</i> Tracking System. [PDF]

Biosensors (Basel)
Escobar-Benavides S, Peñaranda-Jara JJ, Puchalt JC, Sánchez-Salmerón AJ.   +3 more
europepmc   +1 more source

Advances and Perspectives in Graphene‐Based Quantum Dots Enabled Neuromorphic Devices

Advanced Science, EarlyView.
Graphene‐based QDs are zero‐dimensional carbon nanomaterials with pronounced quantum confinement and tunable electronic structures. Herein, we summarize their synthesis strategies and functionalization methods, and highlight their functional roles and operating mechanisms in devices, as well as recent advances in neuromorphic electronics. We anticipate
Yulin Zhen, Wei Zeng, Zherui Zhao, JiYu Zhao, Ziyan Huang, Zhuo Chen, Kui Zhou, Guanglong Ding, Su‐Ting Han, Ye Zhou   +9 more
wiley   +1 more source

Objective interpretation of intrapartum cardiotocography images using attention-guided convolutional neural networks. [PDF]

Front Pediatr
Zhou X, Qiu T, Lin C, Zhou J, Jiang S, Wang L, Feng L, Wang X, You Q.   +8 more
europepmc   +1 more source

Deep Volumetric Super‐Resolution Imaging in Thick Biological Specimens With Sparse Scanning SIM

Advanced Science, EarlyView.
Sparse scanning structured illumination microscopy (SS‐SIM) extends the axial depth in super‐resolution fluorescence imaging by combining rapid laser scanning, pixel‐addressed intensity modulation, and sCMOS camera detection. SS‐SIM yields 1.6× lateral and 1.7× axial resolution gains over wide‐field microscopy and enables imaging through 300–600 µm ...
Sha An, Xuhong Guo, Zhongxia Cai, Yunze Lei, Kai Wen, Hongfei Suo, Xiaoyu Kong, Gerd Ulrich Nienhaus, Peng Gao   +8 more
wiley   +1 more source

Enhancing WSI image classification with graph convolutional neural networks and model uncertainty modeling. [PDF]

BMC Med Imaging
Liu C, Cheng Y, Li T, Hao Y, Zhang Q.
europepmc   +1 more source