Search Results - Python graph embedding methods GEM library

Rapid high-fidelity quantum simulations using multi-step nonlinear autoregression and graph embeddings.

Authors: Shah, Akeel A. Leung, P. K. Xing, W. W.

Source: NPJ Computational Materials; 3/2/2025, Vol. 11 Issue 1, p1-13, 13p

Subject Terms: ARTIFICIAL intelligence, AUTOREGRESSIVE models, MACHINE learning, HIGH throughput screening (Drug development), DIPOLE moments

Graph embedding techniques, applications, and performance: A survey.

Authors: Goyal, Palash Ferrara, Emilio

Source: Knowledge-Based Systems. Jul2018, Vol. 151, p78-94. 17p.

Subject Terms: *FACTORIZATION, *GRAPH theory, *EMBEDDINGS (Mathematics), *RANDOM walks, *DEEP learning

Unifying multi-sample network inference from prior knowledge and omics data with CORNETO.

Authors: Rodriguez-Mier, Pablo Garrido-Rodriguez, Martin Gabor, Attila et al.

Source: Nature Machine Intelligence; Jul2025, Vol. 7 Issue 7, p1168-1186, 19p

Resolving data bias improves generalization in binding affinity prediction.

Authors: Graber, David Stockinger, Peter Meyer, Fabian et al.

Source: Nature Machine Intelligence; Oct2025, Vol. 7 Issue 10, p1713-1725, 13p

A Survey on Embedding Dynamic Graphs.

Authors: BARROS, CLAUDIO D. T. MENDONÇA, MATHEUS R. F. VIEIRA, ALEX B. et al.

Source: ACM Computing Surveys; Jan2023, Vol. 55 Issue 1, p1-37, 37p

Subject Terms: VECTOR spaces, RANDOM walks, POINT processes, MATRIX decomposition, FACTORIZATION

Comparative multi-omics evaluation of the tumor microenvironment in dog and human hepatocellular carcinoma.

Authors: Nussbaum, Yulia I. Bryan, Jeffrey N. Li, Guangfu et al.

Source: Veterinary Oncology (3004-9814). 10/23/2025, Vol. 2 Issue 1, p1-13. 13p.

A Survey on Embedding Dynamic Graphs.

Authors: BARROS, CLAUDIO D. T. MENDONÇA, MATHEUS R. F. VIEIRA, ALEX B. et al.

Source: ACM Computing Surveys; Jan2022, Vol. 54 Issue 1, p1-37, 37p

Subject Terms: REPRESENTATIONS of graphs, VECTOR spaces, RANDOM walks, POINT processes, MATRIX decomposition

Graph Embedding Techniques, Applications, and Performance: A Survey

Authors: Goyal, Palash Ferrara, Emilio

Index Terms: Computer Science - Social and Information Networks, Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability, text

URL: http://arxiv.org/abs/1705.02801

Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.

Authors: Wang XY Chen Y Li YF et al.

Source: Acta pharmacologica Sinica [Acta Pharmacol Sin] 2025 Nov; Vol. 46 (11), pp. 2865-2876. Date of Electronic Publication: 2025 Jun 17.

Publication Type: Journal Article; Review

Journal Info: Publisher: Nature Publishing Group Country of Publication: United States NLM ID: 100956087 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1745-7254 (Electronic) Linking ISSN: 16714083 NLM ISO Abbreviation: Acta Pharmacol Sin Subsets: MEDLINE

MeSH Terms: Drug Discovery*/methods , Artificial Intelligence*, Humans ; Proteins/chemistry ; Proteins/metabolism ; Animals ; Drug Design ; Ligands

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Authors: Ashoor, Haitham Chen, Xiaowen Rosikiewicz, Wojciech et al.

Source: Nature Communications; 3/3/2020, Vol. 11 Issue 1, p1-11, 11p

Subject Terms: CHROMATIN, HIDDEN Markov models, REINFORCEMENT learning, COMPUTATIONAL biology, DEEP learning, EMBEDDINGS (Mathematics), TRANSCRIPTION factors, GRAPH algorithms

UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines.

Authors: Guo, Qianrong Hernandez-Hernandez, Saiveth Ballester, Pedro J.

Source: Journal of Cheminformatics; 6/10/2025, Vol. 17 Issue 1, p1-18, 18p

Subject Terms: VIRTUAL high-throughput screening (Drug development), CHEMICAL libraries, DRUG discovery, ARTIFICIAL intelligence, TRANSFORMER models

FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials.

Authors: Plé, Thomas Adjoua, Olivier Lagardère, Louis et al.

Source: Journal of Chemical Physics; 7/28/2024, Vol. 161 Issue 4, p1-16, 16p

Subject Terms: MOLECULAR dynamics, GRAPHICS processing units, AUTOMATIC differentiation, MACHINE learning, MULTIPURPOSE buildings

Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network.

Authors: Zhang, Xun Qian, Kun Li, Hongwei

Source: Briefings in Bioinformatics; Nov2024, Vol. 25 Issue 6, p1-14, 14p

Subject Terms: GRAPH neural networks, DATA integration, RNA sequencing, GENES

An edge sensitivity based gradient attack on graph isomorphic networks for graph classification problems.

Authors: Srinivasan, Srinitish OmKumar, Chandraumakantham

Source: Scientific Reports; 4/29/2025, Vol. 15 Issue 1, p1-14, 14p

Subject Terms: GRAPH neural networks, REPRESENTATIONS of graphs, RESEARCH personnel, POPULARITY

Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation.

Authors: Lee, Taeyeub Posma, Joram M.

Source: Journal of Cheminformatics; 8/18/2025, Vol. 17 Issue 1, p1-17, 17p

Subject Terms: GRAPH neural networks, DRUG development, HEPATOTOXICOLOGY, MOLECULAR models, MOLECULAR graphs, TOXICITY testing, CHEMICAL properties

REVOLT-Upgrade: a software tool for fast, energy-resolved simulations of soft x-ray detectors in nuclear fusion experiments.

Authors: Guiotto, F Celora, A Croci, G et al.

Source: Measurement Science & Technology; Jul2025, Vol. 36 Issue 7, p1-20, 20p

Subject Terms: NUCLEAR counters, NUCLEAR fusion, PHOTOMULTIPLIERS, MONTE Carlo method, X-ray imaging

A contrastive adversarial encoder for multi-omics data integration.

Authors: Yinghua, Ma Khan, Ahmad Heng, Yang et al.

Source: PLoS ONE; 10/16/2025, Vol. 20 Issue 10, p1-16, 16p

Subject Terms: MULTIOMICS, DATA integration, GENERATIVE adversarial networks, TRANSFORMER models, INDIVIDUALIZED medicine, MACHINE learning, EARLY detection of cancer

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra‐large chemical libraries.

Authors: Popov, Konstantin I. Wellnitz, James Maxfield, Travis et al.

Source: Molecular Informatics; Jan2024, Vol. 43 Issue 1, p1-16, 16p

Subject Terms: CHEMICAL libraries, WORKFLOW, MOLECULAR docking, HIGH throughput screening (Drug development)

An imbalanced learning method based on graph tran-smote for fraud detection.

Authors: Wen, Jintao Tang, Xianghong Lu, Jianguang

Source: Scientific Reports; 7/17/2024, Vol. 14 Issue 1, p1-13, 13p

Subject Terms: FRAUD investigation, GRAPH neural networks, FRAUD, TRANSFORMER models, SOCIAL stability

Generative haplotype prediction outperforms statistical methods for small variant detection in next-generation sequencing data.

Authors: O'Fallon, Brendan Bolia, Ashini Durtschi, Jacob et al.

Source: Bioinformatics; Nov2024, Vol. 40 Issue 11, p1-9, 9p

Subject Terms: SINGLE nucleotide polymorphisms, LANGUAGE models, DE Bruijn graph, WHOLE genome sequencing, HAPLOTYPES