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24 papers · ranked by Valyu relevance

01
PPubMed1 Aug 2025
SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction Protein function prediction with supervised graph learning
Shugang Zhang, Yuntong Li, Wenjian Ma, Qing Cai + 6 more
'Xiangpeng Bi' 'Huasen Jiang' 'Xiaoyu Huang' 'Zhiqiang Wei' 'Wei Lan'] Understanding the functions of proteins is of great importance for deciphering the mechanisms of life activities. To date, there have been over 200 million known proteins, but only 0.2% of them have well-annotated functional terms. By measuring the…
89relevance
02
AarXiv2022
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
Dongkwan Kim, Alice Oh
Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when graphs are noisy. In this paper, we propose a self-supervised graph attention network (SuperGAT), an…
85relevance
03
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
83relevance
04
PPubMed4 Feb 2021
Survey on graph embeddings and their applications to machine learning problems on graphs
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
81relevance
05
BbioRxiv1 Jan 2025
GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics
Yuxi Liu, Zhenhao Zhang, Mufan Qiu, Song Wang + 5 more
Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure across cells and genes remains underexploited, especially in the presence of technical noise and sparsity. Effectively leveraging this multi-scale structure is essentially an…
81relevance
06
BbioRxiv28 Sep 2020
Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks
Juexin Wang, Anjun Ma, Qin Ma, Dong Xu + 1 more
Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem…
80relevance
07
PPubMed14 Jan 2026
Self-supervised learning on graphs predicts non-coding RNA and disease associations
Qingwen Wu, Sujuan Tang
Non-coding RNAs (ncRNAs) play crucial roles in regulating the initiation and progression of various cancers. Accurate identification disease-related ncRNAs would provide a unique opportunity to design better therapeutic interventions. Graph convolutional network-based methods have been proposed to identify potential…
79relevance
08
PPubMed6 Dec 2017
A comparison of graph- and kernel-based –omics data integration algorithms for classifying complex traits
Kang K. Yan, Hongyu Zhao, Herbert Pang
Background High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic…
78relevance
09
PPubMed28 Mar 2021
Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning
Xun Zhang, Lanyan Yang, Bin Zhang, Ying Liu + 4 more
'Xiaohai Qin' 'Mengmeng Hao' 'Jaesung Lee'] The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to…
78relevance
10
AarXiv2018
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li, Zhichao Han, Xiao-Ming Wu
Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN…
78relevance
11
CchemRxiv5 Jun 2023
Improved Environmental Chemistry Property Prediction of Molecules with Graph Machine Learning
Shang Zhu, Bichlien H. Nguyen, Yingce Xia, Kali Frost + 3 more
Rapid prediction of environmental chemistry properties is critical towards the green and sustainable development of chemical industry and drug discovery. Machine learning methods can be applied to learn the relations between chemical structures and their environmental impact. Graph machine learning, by learning the…
77relevance
12
CchemRxiv6 Oct 2025
Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
76relevance
13
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
76relevance
14
BbioRxiv11 Dec 2022
Graph neural networks learn emergent tissue properties from spatial molecular profiles
David S. Fischer, Mayar Ali, Sabrina Richter, Ali Ertürk + 1 more
Tissue phenotypes such as metabolic states, inflammation, and tumor properties are functions of molecular states of cells that constitute the tissue. Recent spatial molecular profiling assays measure tissue architecture motifs in a molecular and often unbiased way and thus can explain some aspects of emergence of these…
74relevance
15
PPubMed29 Nov 2023
BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation
Zhen Wang, Zheng Feng, Yanjun Li, Bowen Li + 4 more
'Chulin Sha' 'Min He' 'Xiaolin Li'] Title: Abstract Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules.…
74relevance
16
CchemRxiv8 Jul 2025
Efficient learning of molecular properties using Graph Neural Networks enhanced with chemistry knowledge
Authors not listed
Graph Neural Networks (GNNs) have emerged as a powerful tool in predicting molecular properties based on structural data. While GNNs excel in identifying local patterns within molecules, their ability to capture global properties remains limited due to inherent structural challenges such as oversmoothing and their…
74relevance
17
AarXiv2016
Semi-Supervised Learning via Sparse Label Propagation
Alexander Jung, Alfred O. Hero, Alexandru Mara, Saeed Jahromi
This work proposes a novel method for semi-supervised learning from partially labeled massive network-structured datasets, i.e., big data over networks. We model the underlying hypothesis, which relates data points to labels, as a graph signal, defined over some graph (network) structure intrinsic to the dataset.…
74relevance
18
BbioRxiv2 Oct 2023
PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning
Yihe Deng, Ruochi Zhang, Pan Xu, Jian Ma + 1 more
Hypergraphs are powerful tools for modeling complex interactions across various domains, including biomedicine. However, learning meaningful node representations from hypergraphs remains a challenge. Existing supervised methods often lack generalizability, thereby limiting their real-world applications. We propose a…
73relevance
19
AarXiv2017
An experimental study of graph-based semi-supervised classification with additional node information
Bertrand Lebichot, Marco Saerens
The volume of data generated by internet and social networks is increasing every day, and there is a clear need for efficient ways of extracting useful information from them. As those data can take different forms, it is important to use all the available data representations for prediction. In this paper, we focus our…
72relevance
20
AarXiv2019
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Pantelis Elinas, Edwin V. Bonilla, Louis C. Tiao
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and…
71relevance
21
CchemRxiv6 Mar 2025
Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data
Authors not listed
Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training data. However, the real-world applications of such…
71relevance
22
BbioRxiv25 Sep 2025
Exploring Augmentation-Driven Invariances for Graph Self-supervised Learning in Spatial Omics
Lovro Rabuzin, Michel Tarnow, Valentina Boeva
Spatial omics technologies provide rich insights into biological processes by jointly capturing molecular profiles and the spatial organization of cells. The resulting high-dimensional data can be naturally represented as graphs, where Graph Neural Networks (GNNs) offer an effective framework to model interactions in…
69relevance
23
AarXiv2023
Graph Sphere: From Nodes to Supernodes in Graphical Models
Willem van den Boom, Maria De Iorio, Alexandros Beskos, Ajay Jasra
High-dimensional data analysis typically focuses on low-dimensional structure, often to aid interpretation and computational efficiency. Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data by representing variables as nodes and dependencies as edges.…
69relevance
24
AarXiv11 Feb 2025
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
Rudrajit Dawn, Madhusudan Ghosh, Partha Basuchowdhuri, Sudip Kumar Naskar
Problems Authors: ['Rudrajit Dawn' 'Madhusudan Ghosh' 'Partha Basuchowdhuri' 'Sudip Kumar Naskar'] Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image.…
68relevance

Results for supervised graph models

SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction Protein function prediction with supervised graph learning

How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision

Multi-fidelity machine learning models for improved high-throughput screening predictions

Survey on graph embeddings and their applications to machine learning problems on graphs

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

Self-supervised learning on graphs predicts non-coding RNA and disease associations

A comparison of graph- and kernel-based –omics data integration algorithms for classifying complex traits

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

Improved Environmental Chemistry Property Prediction of Molecules with Graph Machine Learning

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Graph neural networks learn emergent tissue properties from spatial molecular profiles

BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation

Efficient learning of molecular properties using Graph Neural Networks enhanced with chemistry knowledge

Semi-Supervised Learning via Sparse Label Propagation

PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

An experimental study of graph-based semi-supervised classification with additional node information

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data

Exploring Augmentation-Driven Invariances for Graph Self-supervised Learning in Spatial Omics

Graph Sphere: From Nodes to Supernodes in Graphical Models

Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems

SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction Protein function prediction with supervised graph learning

How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision

Multi-fidelity machine learning models for improved high-throughput screening predictions

Survey on graph embeddings and their applications to machine learning problems on graphs

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

Self-supervised learning on graphs predicts non-coding RNA and disease associations

A comparison of graph- and kernel-based –omics data integration algorithms for classifying complex traits

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

Improved Environmental Chemistry Property Prediction of Molecules with Graph Machine Learning

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Graph neural networks learn emergent tissue properties from spatial molecular profiles

BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation

Efficient learning of molecular properties using Graph Neural Networks enhanced with chemistry knowledge

Semi-Supervised Learning via Sparse Label Propagation

PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

An experimental study of graph-based semi-supervised classification with additional node information

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data

Exploring Augmentation-Driven Invariances for Graph Self-supervised Learning in Spatial Omics

Graph Sphere: From Nodes to Supernodes in Graphical Models

Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems