ArticleMolecular biology research communications2025
Comprehensive computational analysis of deleterious nsSNPs in
Article in Molecular biology research communications, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Molecular identification ofMolecular biology research communications · 2025Article
- A systematic in-silico functional and structural analysis reveals deleterious missense nsSNPs in the humanMolecular biology research communications · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single nucleotide polymorphisms (SNPs) are pivotal in understanding the genetic basis of complex disorders. Among them, nonsynonymous SNPs (nsSNPs) that alter amino acid sequences can significantly impact protein structure and function. This study focuses on analyzing deleterious nsSNPs in the tumor suppressor gene
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.