ArticleBioinformation2010
Early diagnosis of systemic lupus erythmatosus using ANN models of dsDNA binding antibody sequence data.
Article in Bioinformation, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Pivotal Role of Quantum Dots in the Advancement of Healthcare Research.Computational intelligence and neuroscience · 2021Review
- Application of Various Statistical Models to Explore Gene-Gene Interactions in Folate, Xenobiotic, Toll-Like Receptor and STAT4 Pathways that Modulate Susceptibility to Systemic Lupus Erythematosus.Molecular diagnosis & therapy · 2016Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper a new method based on artificial neural networks (ANN), is introduced for identifying pathogenic antibodies in Systemic Lupus Erythmatosus (SLE). dsDNA binding antibodies have been implicated in the pathogenesis of this autoimmune disease. In order to identify these dsDNA binding antibodies, the protein sequences of 42 dsDNA binding and 608 non-dsDNA binding antibodies were extracted from Kabat database and encoded using a physicochemical property of their amino acids namely Hydrophilicity. Encoded antibodies were used as the training patterns of a general regression neural network (GRNN). Simulation results show that the accuracy of proposed method in recognizing dsDNA binding antibodies is 83.2%. We have also investigated the roles of the light and heavy chains of anti-dsDNA antibodies in binding to DNA. Simulation results concur with the published experimental findings that in binding to DNA, the heavy chain of anti-dsDNA is more important than their light chain.
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Registered trials
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