Suchergebnisse - Python graph embedding methods GEM library*

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

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

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

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

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

Autoren: Goyal, Palash Ferrara, Emilio

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

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

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

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

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

Resolving data bias improves generalization in binding affinity prediction.

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

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

A Survey on Embedding Dynamic Graphs.

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

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

Schlagwörter: VECTOR spaces, RANDOM walks, POINT processes, MATRIX decomposition, FACTORIZATION

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

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

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

A Survey on Embedding Dynamic Graphs.

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

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

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

Graph Embedding Techniques, Applications, and Performance: A Survey

Autoren: Goyal, Palash Ferrara, Emilio

Index Begriffe: 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.

Autoren: Wang XY Chen Y Li YF et al.

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

Publikationsart: Journal Article; Review

Info zur Zeitschrift: 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-Schlagworte: 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.

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

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

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

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

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

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

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

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

Autoren: Zhang, Xun Qian, Kun Li, Hongwei

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

Schlagwörter: GRAPH neural networks, DATA integration, RNA sequencing, GENES

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

Autoren: Srinivasan, Srinitish OmKumar, Chandraumakantham

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

Schlagwörter: 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.

Autoren: Lee, Taeyeub Posma, Joram M.

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

Schlagwörter: 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.

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

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

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

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

Autoren: Wen, Jintao Tang, Xianghong Lu, Jianguang

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

Schlagwörter: 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.

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

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

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

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

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

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

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

BeGin: Extensive Benchmark Scenarios and an Easy-to-use Framework for Graph Continual Learning.

Autoren: Ko, Jihoon Kang, Shinhwan Kwon, Taehyung et al.

Quelle: ACM Transactions on Intelligent Systems & Technology; Feb2025, Vol. 16 Issue 1, p1-22, 22p

Schlagwörter: ELECTRONIC data processing, MODULAR design, ALGORITHMS

Construction of a Geological Fault Corpus and Named Entity Recognition.

Autoren: Wang, Huainuo Niu, Ruiqing Han, Yongyao et al.

Quelle: Applied Sciences (2076-3417); Mar2025, Vol. 15 Issue 5, p2465, 18p

Schlagwörter: LANGUAGE models, KNOWLEDGE graphs, INVESTIGATION reports, GEOLOGICAL research, DEEP learning