28 papers · ranked by Valyu relevance
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
Xiao-Meng Zhang, Li Liang, Lin Liu, Ming-Jing Tang
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of…
Mugang Lin, Kunhui Wen, Xuanying Zhu, Huihuang Zhao + 4 more
'Yongpan Sheng' 'Hao Wang' 'Yixiang Fang'] The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while…
Teja Potu, Yunfei Hu, Rituparna Khan, Srinija Dharani + 4 more
Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognosis and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and responses to drug and treatment. While the low coverage scDNA-seq…
Jalal Mirakhorli, Mojgan Mirakhorli
Functional neuroimaging techniques using resting-state functional MRI (rs-fMRI) have accelerated progress in brain disorders and dysfunction studies. Since, there are the slight differences between healthy and disorder brains, investigation in the complex topology of human brain functional networks is difficult and…
Paul Krzakala, Gabriel Melo, Charlotte Laclau, Florence d'Alché-Buc + 1 more
'Rémi Flamary'] Although graph-based learning has attracted a lot of attention, graph representation learning is still a challenging task whose resolution may impact key application fields such as chemistry or biology. To this end, we introduce GRALE, a novel graph autoencoder that encodes and decodes graphs of varying…
Shi Han, Haozheng Fan, James T. Kwok
The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graphstructured data. While the encoder is often a powerful graph convolutional network, the decoder reconstructs the graph structure by only considering two nodes at a time, thus ignoring possible interactions…
Jiwoong Park, Minsik Lee, Hyung Jin Chang, Kyuewang Lee + 1 more
'Jin Young Choi'] We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric…
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…
Liam K. Magargal, Parisa Khodabakhshi, Steven N. Rodriguez, Justin Jaworski + 1 more
graph autoencoders Authors: ['Liam K. Magargal' 'Parisa Khodabakhshi' 'Steven N. Rodriguez' 'Justin Jaworski' 'John G. Michopoulos'] This paper presents a graph autoencoder architecture capable of performing projection-based model-order reduction (PMOR) on advection-dominated flows modeled by unstructured meshes. The…
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros + 1 more
We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the…
Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong + 6 more
Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN…
Zixiang Luo, Chenyu Xu, Zhen Zhang, Wenfei Jin
Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE), a…
Md Toki Tahmid, Tanjeem Azwad Zaman, Mohammad Saifur Rahman
Understanding complex graph-structured data is a cornerstone of modern research in fields like cheminformatics and bioinformatics, where molecules and biological systems are naturally represented as graphs. However, traditional graph neural networks (GNNs) often fall short by focusing mainly on node features while…
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis
'Michalis Vazirgiannis'] In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train models only from a dense subset of nodes instead of using the entire graph. Together with a simple yet effective…
Xiang Feng, Fang Fang, Haixia Long, Rao Zeng + 1 more
With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important.…
Chengxin Xie, Jingui Huang, Yongjiang Shi, Hui Pang + 3 more
'Xiumei Wen' 'Marco Piangerelli'] Graph auto-encoders are a crucial research area within graph neural networks, commonly employed for generating graph embeddings while minimizing errors in unsupervised learning. Traditional graph auto-encoders focus on reconstructing minimal graph data loss to encode neighborhood…
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering. Graph AE, VAE and most of their extensions rely on multi-layer graph convolutional networks (GCN)…
Hakan Gunduz, Muhammad Aleem
Malware harms the confidentiality and integrity of the information that causes material and moral damages to institutions or individuals. This study proposed a malware detection model based on API-call graphs and used Graph Variational Autoencoder (GVAE) to reduce the size of graph node features extracted from Android…
Lijun Liu, Xiaoyang Wu, Jun Yu, Yuduo Zhang + 3 more
'Edward L. Braun'] Simple Summary Due to the rapid development of single-cell RNA sequencing technology, the volume of single-cell RNA sequencing data has grown exponentially. Traditional clustering methods have proven increasingly difficult to cluster this large-scale and highly complex single-cell RNA sequencing…
David Buterez, Ioana Bica, Ifrah Tariq, Helena Andrés-Terré + 1 more
Currently, single-cell RNA sequencing (scRNA-seq) allows high-resolution views of individual cells, for libraries of up to (tens of) thousands of samples. In this study, we introduce the use of graph neural networks (GNN) in the unsupervised study of scRNA-seq data, namely for dimensionality reduction and clustering.…
Vladimir Kondratyev, Marian Dryzhakov, Timur Gimadiev, Dmitriy Slutskiy
In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset and further optimization with a regression model led to a latent space that can…
Mingyuan Ma, Sen Na, Hongyu Wang
We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convolutional network operating directly on graphs, whose hidden layers are constrained by an autoencoder.…
Azka Javaid, H. Robert Frost
We propose an autoencoder-based framework for denoising networks estimated from Spatial Transcriptomics (ST) data for cell signaling analysis. Our method consists of an unsupervised encoder-decoder framework for denoising the network adjacency matrix and a supervised framework for cell signaling estimation. We validate…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
Pavel Kohout, Michal Vasina, Marika Majerova, Veronika Novakova + 5 more
Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights, leveraging homologous sequences to pinpoint conserved and functionally…
Authors not listed
The accurate representation of atoms within their environment forms the backbone of any reliable machine learning force field (MLFF). While modern MLFFs treat atoms of the same type as indistinguishable, their identities can be further resolved by accounting for the composition of their chemical environment. This can…