Results 171 to 180 of about 9,194 (254)

Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis. [PDF]

Sci Rep
Yadalam PK, Natarajan PM, Ardila CM.
europepmc   +1 more source

Big Data in Cancer Genomics: Computational Foundations and Emerging Pathways for Precision Oncology

Computational and Systems Oncology, Volume 6, Issue 1, December 2026.
ABSTRACT This review aims to explore the computational foundations of big data in cancer genomics and examine emerging pathways that support precision oncology and personalized cancer care. A narrative review approach was adopted to synthesize evidence from PubMed, Scopus, Web of Science, and IEEE Xplore.
Nur Vanu, Nur Mohammad, Fahad Ahmed, Towsif Alam, Koushik Saha, Durga Shahi, Adib Hossain, Mia Md. Tofayel Gonee Manik, Mst. Rina Parvin   +8 more
wiley   +1 more source

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines. [PDF]

Front Pharmacol
Wang Z, Chen Y, Ma H, Gao H, Zhu Y, Wang H, Zhang N.   +6 more
europepmc   +1 more source

Graph Autoencoder for Process Monitoring

To improve the reliability and interpretability of industrial process monitoring, this article proposes a Causal Graph Spatial-Temporal Autoencoder (CGSTAE). The network architecture of CGSTAE combines two components: a correlation graph structure learning module based on spatial self-attention mechanism (SSAM) and a spatial-temporal encoder-decoder ...
openaire   +2 more sources

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Spatiotemporal associations between air pollution and emergency room visits for cardiovascular and cerebrovascular diseases in Korea using a multivariate graph autoencoder modeling approach: an ecological study. [PDF]

Ewha Med J
Wang S, Jeong S, Ha E.
europepmc   +1 more source

Advances in causal discovery methods for ecological time series

Biological Reviews, Volume 101, Issue 5, Page 2313-2336, October 2026.
ABSTRACT Recent advances in data collection technologies (e.g. automated sensor networks, satellite remote sensing, and high‐throughput sequencing) have greatly expanded the availability of ecological time series, enabling new opportunities for causal analyses in dynamic ecosystems.
Kenta Suzuki, Masato Yamamichi, Yutaka Osada, Masayuki Ushio, Kohei Nakajima, Hiroshi Masuya, Shohei Shimizu   +6 more
wiley   +1 more source

VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell-cell communication network. [PDF]

Brief Bioinform
Zhang T, Zhang X, Wu Z, Ren J, Zhao Z, Zhang H, Wang G, Wang T.   +7 more
europepmc   +1 more source

Relating transcriptomics to protein abundance reveals self‐driven versus interactor‐driven proteins

Protein Science, Volume 35, Issue 10, October 2026.
Abstract Statistically modeling the interdependence between transcriptomics and protein abundances remains a persistent challenge in bioinformatics research. Transcriptomic data and proteomic abundances typically display a moderate Pearson's correlation of about 0.5, while in tumor conditions, the correlation may decrease to a much weaker correlation ...
Loulwah Arnaout, Volkhard Helms, Aram Papazian   +2 more
wiley   +1 more source

scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling. [PDF]

Biol Direct
Wang S, Liu Y, Zhang H, Liu Z.
europepmc   +1 more source