Results 51 to 60 of about 263,942 (258)

Node Classification in Random Trees

We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the tree data structure is associated with node attributes (typically high dimensional embeddings).
Wouter W. L. Nuijten, Vlado Menkovski
openaire   +3 more sources

DynGraph-BERT: Combining BERT and GNN Using Dynamic Graphs for Inductive Semi-Supervised Text Classification

Informatics
The combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with ...
Eliton Luiz Scardin Perin, Mariana Caravanti de Souza, Jonathan de Andrade Silva, Edson Takashi Matsubara   +3 more
doaj   +1 more source

Unifying Structural Proximity and Equivalence for Network Embedding

IEEE Access, 2019
The fundamental purpose of network embedding is to automatically encode each node in a network as a low-dimensional vector, while at the same time preserving certain characteristics of the network.
Benyun Shi, Chunpeng Zhou, Hongjun Qiu, Xiaobin Xu, Jiming Liu   +4 more
doaj   +1 more source

Occurrence of Extramedullary Relapses in Pediatric Acute Lymphoblastic Leukemia After Treatment With Blinatumomab

Pediatric Blood &Cancer, EarlyView.
ABSTRACT We report a retrospective single‐center analysis of pediatric patients with relapsed or refractory B‐cell precursor acute lymphoblastic leukemia focusing on relapses outside of the typical locations, bone marrow, central nervous system, or testes.
Johanna Kunz, Martin Ebinger, Michaela Döring, Markus Mezger, Ursula Holzer, Rupert Handgretinger, Peter Lang, Manon Queudeville   +7 more
wiley   +1 more source

A-B Nodes Classification for Power Estimation [PDF]

2006 International Conference on Field Programmable Logic and Applications, 2006
In this paper, an optimization for the classical statistical power estimation method is proposed. This technique is applied to the individual nodes. The optimization is based on two observations. Firstly, a small percentage of both the nodes and the estimated power requires nearly a half of the total simulation time.
Elias Todorovich, Eduardo I. Boemo
openaire   +1 more source

Node Classification of Imbalanced Data Using Ensemble Graph Neural Networks

Applied Sciences
In real-world scenarios, many datasets suffer from class imbalance. For example, on online review platforms, the proportion of fake and genuine comments is often highly skewed.
Yuan Liang
doaj   +1 more source

Safety of Daprodustat for the Treatment of Chronic Kidney Disease Anemia: Final Analysis of a Multicenter Postmarketing Surveillance Study in Japan

Therapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Introduction This final analysis of a multicenter, prospective postmarketing surveillance study evaluated the safety of daprodustat in patients with chronic kidney disease anemia in routine clinical practice in Japan. Methods Patients who initiated daprodustat between September 2020 and July 2022 were registered.
Tadao Akizawa, Kyomi Kanaya, Kiyomi Aizawa, Masanori Onoki, Hirofumi Ozeki, Kazuko Suzuki, Ai Hayashi, Tomohiko Asamura   +7 more
wiley   +1 more source

Peripheral lysosomes recruit PLEKHG3 to focal adhesions and restrain protrusion dynamics

FEBS Letters, EarlyView.
Proximity‐dependent labeling at the LAMTOR complex revealed the Rho GEF PLEKHG3 as a lysosome‐proximal protein directing the study toward the influence of lysosome positioning on actin dynamics and cell motility. We show that PLEKHG3 colocalizes with lysosomes at focal adhesion sites and observe that forced peripheral dispersion of lysosomes hinders ...
Rainer Ettelt, Ana‐Maria Sandru, Georg Vucak, Sebastian Didusch, Biljana Riemelmoser, Karin Ehrenreiter, Markus Hartl, Lukas A. Huber, Manuela Baccarini   +8 more
wiley   +1 more source

Multi-Duplicated Characterization of Graph Structures Using Information Gain Ratio for Graph Neural Networks

IEEE Access, 2023
Various graph neural networks (GNNs) have been proposed to solve node classification tasks in machine learning for graph data. GNNs use the structural information of graph data by aggregating the feature vectors of neighboring nodes.
Yuga Oishi, Ken Kaneiwa
doaj   +1 more source

The role of miR‐335‐5p in the redifferentiation of BRAF p.V600E thyroid cancers

Molecular Oncology, EarlyView.
The BRAF p.V600E mutation promotes thyroid cancer dedifferentiation and radioiodine resistance. Using a network approach, we identified miR‐335‐5p as a key regulator of BRAF‐mutated thyroid tumors. Restoring miR‐335‐5p increased thyroid‐specific gene expression and iodine uptake in cells and organoids.
Valeria Pecce, Simone Bini, Marialuisa Sponziello, Giorgio Grani, Giulia Fiscon, Paola Paci, Lorenzo Farina, Rosa Falcone, Valentina Maggisano, Sebastiano Filetti, Cosimo Durante, Antonella Verrienti   +11 more
wiley   +1 more source