fdata-02-00002-g0001_Deep Representation Learning for Social Network Analysis.tif
, 2020 Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other ...
Qiaoyu Tan (8541435) +2 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Facioscapulohumeral muscular dystrophy (FSHD) is one of the most debilitating and common muscular dystrophies. Despite its severity, no approved therapy exists for FSHD patients. However, several therapeutic candidates are currently under development, and some have recently entered clinical trials, marking the need for reliable ...
Mustafa Bilal Bayazit +11 more
wiley +1 more source
fdata-02-00002-g0002_Deep Representation Learning for Social Network Analysis.tif
, 2020 Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other ...
Qiaoyu Tan (8541435) +2 more
core +1 more source
Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods. [PDF]
Front Genet, 2022 Sun X +6 more
europepmc +1 more source
Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang +4 more
wiley +1 more source
fdata-02-00002_Deep Representation Learning for Social Network Analysis.xml
, 2020 Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other ...
Qiaoyu Tan (8541435) +2 more
core +1 more source
Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning. [PDF]
Front Genet, 2021 Liu H +9 more
europepmc +1 more source
White Matter Microstructural Abnormalities in Neonatal Onset Genetic Epilepsy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Recent evidence indicates that epilepsy is associated with abnormal white matter. If seizures alter white matter, then the impact upon network function, epileptogenesis, and cognition could be pronounced in neonates undergoing rapid developmental myelination. Neonates with epilepsy due to nonstructural genetic causes provide a unique
Amanda G. Sandoval Karamian +8 more
wiley +1 more source
Tri-party deep network representation
, 2016 Information network mining often requires examination of linkage relationships between nodes for analysis. Recently, network representation has emerged to represent each node in a vector format, embedding network structure, so off-the-shelf machine ...
Wang, Y, Zhang, C, Wu, J, Zhu, X, Pan, S
core
fdata-02-00002-g0004_Deep Representation Learning for Social Network Analysis.tif
, 2020 Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other ...
Qiaoyu Tan (8541435) +2 more
core +1 more source