Results 101 to 110 of about 720,784 (299)

ERα–PADI4–CXCL8 Axis Drives MDSC‐Mediated Immune Evasion in Anaplastic Thyroid Carcinoma

Advanced Science, EarlyView.
MAPK‐driven ligand‐independent ERα activation drives PADI4 transcription and translation. This enzyme then modifies CXCL8 chemokines through citrullination, generating a potent chemoattractant gradient specifically for myeloid‐derived suppressor cells.
Dongdong Zhang, Ying Cai, Junbin Zhang, Jianyong Zhang, Ran Jing, Ming Cai   +5 more
wiley   +1 more source

Semi-supervised learning by constructing query-document heterogeneous information network

Tongxin xuebao, 2014
Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored ...
Yu-feng LIU, Ren-fa LI
doaj   +2 more sources

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

Application of Non-Sparse Manifold Regularized Multiple Kernel Classifier

Mathematics
Non-sparse multiple kernel learning is efficient but not directly able to be applied in a semi-supervised scenario; therefore, we extend it to semi-supervised learning by using a manifold regularization.
Tao Yang
doaj   +1 more source

The deficiency of a regular graph

Discrete Mathematics, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

Cogrowth of regular graphs [PDF]

Proceedings of the American Mathematical Society, 1992
Let G \mathcal {G} be a
openaire   +2 more sources

Entire Regularization Paths for Graph Data [PDF]

, 2007
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary ...
Koji Tsuda, Tsuda, K.
core   +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

Domain-Invariant Label Propagation With Adaptive Graph Regularization

IEEE Access
As an effective machine learning paradigm, domain adaptation (DA) learning aims to enhance the learning performance of the target domain by utilizing other relevant but distinct domain(s) (referred to as the source domain(s)).
Yanning Zhang, Jianwen Tao, Liangda Yan
doaj   +1 more source

An Expertise Transfer Framework For Autonomous Surgical Assistance

Advanced Science, EarlyView.
This study introduces an expertise transfer framework for procedure‐spanning autonomous surgical assistance. By emulating expert logic through hierarchical perception, attention modeling, and knowledge graph‐based decision‐making, the system provides near‐expert surgical view assistance.
Yuan Gao, Guangdi Chu, Wei Jiao, Yuchuan Zhong, Hang Yuan, Chengjun Li, Jinhua Li, Shuxin Wang, Bo Guan, Jianchang Zhao, Lizhi Pan, Haitao Niu, Jianmin Li   +12 more
wiley   +1 more source