Results 171 to 180 of about 737,076 (296)

Disentangling Heterogeneous Molecular Networks for Multi‐Omics‐Driven Cancer Driver Discovery

Advanced Science, EarlyView.
DRIVE integrates PPI topology and pan‐cancer multi‐omics profiles through dual‐view graph disentanglement, contrastive representation learning, and joint optimization. Across six PPI networks, DRIVE outperforms ten baselines and remains robust to structural and annotation perturbations.
Xinjing Gong, Ji Li, Mu Su, Peishen Yu, Te Ma, Ruiyang Zhai, Chenye Zhang, Mengyan Zhang, Yan Zhang   +8 more
wiley   +1 more source

Graph representation and learning for drug administration prediction. [PDF]

BMC Med Inform Decis Mak
Cammarasana S, Patanè G.
europepmc   +1 more source

PrOMet: Accurate and Robust Metal‐Binding Site and Type Prediction

Advanced Science, EarlyView.
PrOMet is a two‐stage deep learning framework for jointly predicting metal‐binding sites and ion identities from protein structures. Surface candidate sites are first pruned, followed by refinement of the most probable sites and metal types. This hierarchical strategy enables robust and accurate metal prediction for cryo‐EM and AlphaFold‐derived ...
Gangeun Park, Chaok Seok, Hahnbeom Park
wiley   +1 more source

On Network-Error Correcting Convolutional Codes under the BSC Edge Error Model

, 2010
Convolutional network-error correcting codes (CNECCs) are known to provide error correcting capability in acyclic instantaneous networks within the network coding paradigm under small field size conditions. In this work, we investigate the performance of
Sundar B, Rajan, Prasad, K
core  

An Adaptive Attention-Driven Quadruplet Deep Hashing Method for Retrieving Histopathological Images. [PDF]

J Imaging
Alizadeh SM, Müller H, Helfroush MS.
europepmc   +1 more source

Two‐Dimensional Flash Memory: Materials, Interfaces, and Storage–Computing Convergence

Advanced Science, EarlyView.
We systematically summarize recent advances in 2D flash memory, highlighting how interface and material engineering can overcome the conventional trade‐offs among speed, power consumption, and reliability in silicon‐based flash, and outlining its potential for high‐performance memory and in‐memory computing applications.
Zhiyuan Wang, Fanyi Meng, Fanying Meng, Shida Huo, Junxi Xu, Yaohui Wang, Yuexuan Ma, Yuan Xie, Xiaodong Hu, Caofeng Pan, Enxiu Wu   +10 more
wiley   +1 more source

Blind recognition of channel codes based on dual-branch feature fusion convolutional neural networks. [PDF]

Sci Rep
Ma Y, Lei Y, Liu C, Wang W, Teng F, Peng C, Jin H, Feng H, Zhang M, Pan Y.   +9 more
europepmc   +1 more source

Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning

Advanced Science, EarlyView.
A structure‐centric generative framework searches voxelated design space directly, coupling a variational autoencoder with multi‐objective optimization and active learning to progressively expand the validated design domain. The discovered nano‐architectures, realized as pyrolytic carbon by two‐photon lithography, deliver 18–23% higher stiffness and 26–
Jinwook Yeo, Peter Serles, Donggeun Park, Julia R. Greer, Seunghwa Ryu   +4 more
wiley   +1 more source

Enhancing image retrieval via Siamese network-based hashing with gated residual connections. [PDF]

Sci Rep
Nazari A, Rahbar K, Beheshtifard Z, Khademi M.   +3 more
europepmc   +1 more source

Flexible Integration of Structurally Diverse Spatial Multi‐Omics With AgaeSMO

Advanced Science, EarlyView.
AgaeSMO is a versatile and robust deep learning framework for flexible cross‐integration of spatial multi‐omics data. Leveraging a GATs network with a graph attention module, it learns joint embedding across diverse modalities, enabling integration of both structurally paired (e.g., spatial transcriptome‐epigenome, transcriptome‐proteome, transcriptome‐
Kunpeng Liu, Jinpo Su, Haotian Zhou, Yilin Shen, Mengke Qin, Yingjie Luo, Tiancai Wang, Zhenglin Chen, Yuanyuan Wang, Xiaokai Ma   +9 more
wiley   +1 more source