Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText.
Kick-your-searchterm to multiple Engines kick-your-query now !>
A dictionary by aggregated review articles of nephrology, medicine and the life sciences
Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

suck abstract from ncbi


10.1093/bib/bbab491

http://scihub22266oqcxt.onion/10.1093/bib/bbab491
suck pdf from google scholar
34849567!8690188!34849567
unlimited free pdf from europmc34849567    free
PDF from PMC    free
html from PMC    free
PDF vom PMID34849567  :  Publisher
PDF vom PMID34849567

suck abstract from ncbi

pmid34849567
Nephropedia Template TP

gab.com Text

Twit Text FOAVip

Twit Text #

English Wikipedia


  • BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution #MMPMID34849567
  • Yang X; Wang W; Ma JL; Qiu YL; Lu K; Cao DS; Wu CK
  • Brief Bioinform 2022[Jan]; 23 (1): ä PMID34849567show ga
  • MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions. RESULTS: We developed BioNet, a deep biological networkmodel with a graph encoder-decoder architecture. The graph encoder utilizes graph convolution to learn latent information embedded in complex interactions among chemicals, genes, diseases and biological pathways. The learning process is featured by two consecutive steps. Then, embedded information learnt by the encoder is then employed to make multi-type interaction predictions between chemicals and genes with a tensor decomposition decoder based on the RESCAL algorithm. BioNet includes 79 325 entities as nodes, and 34 005 501 relations as edges. To train such a massive deep graph model, BioNet introduces a parallel training algorithm utilizing multiple Graphics Processing Unit (GPUs). The evaluation experiments indicated that BioNet exhibits outstanding prediction performance with a best area under Receiver Operating Characteristic (ROC) curve of 0.952, which significantly surpasses state-of-theart methods. For further validation, top predicted CGIs of cancer and COVID-19 by BioNet were verified by external curated data and published literature.
  • |*Computational Biology[MESH]
  • |*Computer Simulation[MESH]
  • |*Models, Biological[MESH]
  • |*Neural Networks, Computer[MESH]


  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    ä 1.23 2022