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Results for node classification

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12 papers · ranked by Valyu relevance

01
BbioRxiv20 Oct 2025
The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomics
Maddalena M Bolognesi, Lorenzo Dall’Olio, Giulio Eugenio Mandelli, Luisa Lorenzi + 7 more
Lymph nodes (LN) are key secondary lymphoid organs (SLO) for a coordinated immune response. They have been extensively characterized by numerous investigative techniques chiefly as single cell suspensions because they are composed of vagile yet crowded hematolymphoid elements, unfriendly to spatial tissue…
73relevance
02
BbioRxiv3 Nov 2022
The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study
Guangyu Wang, Limei Zhang, Lishan Qiao
Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of…
71relevance
03
BbioRxiv15 Sep 2024
Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks
Lida Kanari, Stanislav Schmidt, Francesco Casalegno, Emilie Delattre + 7 more
The shape of neuronal morphologies plays a critical role in determining their dynamical properties and the functionality of the brain. With an abundance of neuronal morphology reconstructions, a robust definition of cell types is important to understand their role in brain functionality. However, an objective…
70relevance
04
BbioRxiv10 Mar 2021
High frequency rTMS modulates functional connectivity nodes and boundaries differently in the human brain
Vladimir Belov, Vladislav Kozyrev, Aditya Singh, Matthew D. Sacchet + 1 more
Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of disorders, e.g. depression. However, it is not yet understood how rTMS alters brain’s functional connectivity. Here we report the changes captured by resting state functional magnetic resonance imaging (rsfMRI)…
68relevance
05
BbioRxiv6 Mar 2019
A topological data analysis based classification method for multiple measurements
Henri Riihimäki, Wojciech Chachólski, Jakob Theorell, Jan Hillert + 1 more
Machine learning models for repeated measurements are limited. Using topological data analysis (TDA), we present a classifier for repeated measurements which samples from the data space and builds a network graph based on the data topology. When applying this to two case studies, accuracy exceeds alternative models…
66relevance
06
BbioRxiv22 Jan 2021
Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s disease
Jiyoung Byun, Yong Jeong
Deep learning frameworks for disease classification using neuroimaging and non-imaging information require the capability of capturing individual features as well as associative information among subjects. Graphs represent the interactions among nodes, which contain the individual features, through the edges in order…
66relevance
07
BbioRxiv24 Nov 2022
Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning
Luca Cappelletti, Lauren Rekerle, Tommaso Fontana, Peter Hansen + 13 more
Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many…
65relevance
08
BbioRxiv15 Aug 2022
Accurately modeling biased random walks on weighted networks using *node2vec+*
Renming Liu, Matthew Hirn, Arjun Krishnan
Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction…
65relevance
09
BbioRxiv30 May 2020
Taxallnomy: Closing gaps in the NCBI Taxonomy
Tetsu Sakamoto, J. Miguel Ortega
NCBI Taxonomy is the main taxonomic source for several bioinformatics tools and databases since all organisms with sequence accessions deposited on INSDC are organized in its hierarchical structure. Despite the extensive use and application of this data source, taking advantage of its taxonomic tree could be…
60relevance
10
BbioRxiv23 Jul 2015
Cancer classification in the genomic era: five contemporary problems
Qingxuan Song, Sofia D Merajver, Jun Z. Li
Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here we discuss the classification of cancer, the process of categorizing cancer subtypes based…
59relevance
11
BbioRxiv10 Apr 2025
treecompareR: Tree Visualizations of Chemical Space
Paul Kruse, Caroline Ring
This paper presents the treecompareR package for R, which provides tools for reproducible visualizations of data through the use of taxonomies. The package builds on developments from ggplot2 and ggtree to provide visualizations tailored for use with taxonomic classification data. Additionally, it provides tools that…
57relevance
12
BbioRxiv14 Sep 2020
Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data
Fei Deng, Jibing Huang, Xiaoling Yuan, Chao Cheng + 1 more
Most of the biomedical datasets, including those of ‘omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine…
54relevance

Results for node classification

The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomics

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

High frequency rTMS modulates functional connectivity nodes and boundaries differently in the human brain

A topological data analysis based classification method for multiple measurements

Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s disease

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

Accurately modeling biased random walks on weighted networks using node2vec+

Taxallnomy: Closing gaps in the NCBI Taxonomy

Cancer classification in the genomic era: five contemporary problems

treecompareR: Tree Visualizations of Chemical Space

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomics

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

High frequency rTMS modulates functional connectivity nodes and boundaries differently in the human brain

A topological data analysis based classification method for multiple measurements

Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s disease

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

Accurately modeling biased random walks on weighted networks using node2vec+

Taxallnomy: Closing gaps in the NCBI Taxonomy

Cancer classification in the genomic era: five contemporary problems

treecompareR: Tree Visualizations of Chemical Space

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Results for node classification

The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomics

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

High frequency rTMS modulates functional connectivity nodes and boundaries differently in the human brain

A topological data analysis based classification method for multiple measurements

Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s disease

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

Accurately modeling biased random walks on weighted networks using *node2vec+*

Taxallnomy: Closing gaps in the NCBI Taxonomy

Cancer classification in the genomic era: five contemporary problems

treecompareR: Tree Visualizations of Chemical Space

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

The normal human lymph node cell classification and landscape defined by high-dimensional spatial proteomics

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

High frequency rTMS modulates functional connectivity nodes and boundaries differently in the human brain

A topological data analysis based classification method for multiple measurements

Graph neural network based heterogeneous propagation scheme for classifying alzheimer’s disease

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

Accurately modeling biased random walks on weighted networks using *node2vec+*

Taxallnomy: Closing gaps in the NCBI Taxonomy

Cancer classification in the genomic era: five contemporary problems

treecompareR: Tree Visualizations of Chemical Space

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Accurately modeling biased random walks on weighted networks using node2vec+

Accurately modeling biased random walks on weighted networks using node2vec+