Results 41 to 50 of about 78,912 (260)

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

Graph Representations for Reinforcement Learning

Journal of Computer Science and Technology
Graph analysis is becoming increasingly important due to the expressive power of graph models and the efficient algorithms available for processing them. Reinforcement Learning is one domain that could benefit from advancements in graph analysis, given that a learning agent may be integrated into an environment that can be represented as a graph ...
Esteban Schab, Carla Casanova, Fabiana Piccoli   +2 more
openaire   +3 more sources

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

Frontiers in Genetics, 2021
It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data.
Jian Liu, Jian Liu, Shuguang Ge, Shuguang Ge, Yuhu Cheng, Yuhu Cheng, Xuesong Wang, Xuesong Wang   +7 more
doaj   +1 more source

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas F. Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +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

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

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

Representation Learning for Dynamic Graphs: A Survey

J. Mach. Learn. Res., 2019
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs.
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain 0001, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart   +6 more
openaire   +4 more sources

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source

Visual evaluation of graph representation learning based on the presentation of community structures

Visual Informatics
Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization, random walk, and deep learning. However, choosing the right method for different tasks can be challenging.
Yong Zhang, Lihong Cai, Yuhua Liu, Yize Li, Songyue Li, Yuming Ma, Yuwei Meng, Zhiguang Zhou   +7 more
doaj   +1 more source