Results 41 to 50 of about 537,310 (265)

Unsupervised Graph Representation Learning With Variable Heat Kernel

IEEE Access, 2020
Graph representation learning aims to learn a low-dimension latent representation of nodes, and the learned representation is used for downstream graph analysis tasks. However, most of the existing graph embedding models focus on how to aggregate all the
Yongjun Jing, Hao Wang, Kun Shao, Xing Huo, Yangyang Zhang   +4 more
doaj   +1 more source

Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence

, 2018
Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications.
Lin, Fen, Lin, Leyu, Liu, Zhiyuan, Xie, Ruobing   +3 more
core   +1 more source

GFF-CARVING: Graph Feature Fusion for the Recognition of Highly Varying and Complex Balinese Carving Motifs

IEEE Access, 2022
The recognition of Balinese carving motifs is challenging due to the highly varying and interrelated motifs of Balinese carvings and in addition to the scantiness of Balinese carving data.
I Wayan Agus Surya Darma, Nanik Suciati, Daniel Siahaan   +2 more
doaj   +1 more source

Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition

, 2011
This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e.
Balasubramanian, Belkin, Blei, Cheng, Donoho, Hofmann, Jenatton, Lafon, Liu, Lu, Niebles, Olshausen, Parameswaran, Tibshirani, Turaga, Wang, Wright, Yan, Yuxin Peng, Zhiwu Lu   +19 more
core   +1 more source

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

, 2019
Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs.
Ding, Ying, Foote, Brian, Fu, Gang, Gao, Zheng, Gessner, Christopher, Liu, Xiaozhong, Ouyang, Chunping, Tsutsui, Satoshi, Wild, David, Yang, Jeremy, Yu, Qi   +10 more
core   +1 more source

Dual Graph Representation Learning

, 2020
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs.
Zhu, Huiling, Luo, Xin, Zhuo, Hankz Hankui   +2 more
openaire   +2 more sources

Graph-based Molecular Representation Learning

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable
Guo, Zhichun, Guo, Kehan, Nan, Bozhao, Tian, Yijun, Iyer, Roshni G., Ma, Yihong, Wiest, Olaf, Zhang, Xiangliang, Wang, Wei, Zhang, Chuxu, Chawla, Nitesh V.   +10 more
openaire   +2 more sources

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications

, 2019
Over the last few years, machine learning over graph structures has manifested a significant enhancement in text mining applications such as event detection, opinion mining, and news recommendation.
Bijari, Kayvan, Kebriaei, Emad, Veisi, Hadi, Zare, Hadi   +3 more
core   +1 more source

A Depolarizing Leak in Sodium Bicarbonate Cotransporter NBCe1 Causes Brain Edema

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives SLC4A4 encodes electrogenic sodium bicarbonate cotransporter NBCe1, prominently expressed in kidney and brain. Recessive loss‐of‐function variants in SLC4A4 cause proximal renal tubular acidosis, no brain edema. In the brain, NBCe1 is expressed by astrocytes, where it regulates pH and mediates astrocyte volume changes.
Quinty Bisseling, Mark D. Parker, Sven Kerst, Richard A. Pasternack, Jacob Tondreau, Marjolein Breur, Gemma M. van Rooijen‐van Leeuwen, Davide Tonduti, Ettore Salsano, Alejandra Darling, Joanna A. E. van Wijk, Susanna Törnroth‐Horsefield, Marianna Bugiani, Petra J. W. Pouwels, Quinten Waisfisz, Marjo S. van der Knaap, Rogier Min   +16 more
wiley   +1 more source