ArticleJournal of integrative bioinformatics2025
Leveraging transformers for semi-supervised pathogenicity prediction with soft labels.
Article in Journal of integrative bioinformatics, 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
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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.
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Who cites it
2 citing papers in PubMed.
- Exploratory plasma ctDNA genomic biomarkers identified by whole-exome sequencing and a novel bioinformatics pipeline in advanced driver-negative NSCLC.Scientific reports · 2026Observational
- Micro- and Nanoplastics and Functional Nutrients in Human Health: Epigenetic Mechanisms and Cellular Resilience Signaling in Brain Insulin Resistance and the Risk of Alzheimer's Disease.International journal of molecular sciences · 2025Review
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of Next-Generation Sequencing (NGS) technologies has revolutionized the field of genomics, producing large volumes of data that necessitate sophisticated analytical techniques. This paper introduces a Deep Learning model designed to predict the pathogenicity of genetic variants, a vital component in advancing personalized medicine. The model is trained on a dataset derived from the analysis of NGS outputs, containing a combination of well-defined and ambiguous genetic variants. By employing a semi-supervised learning approach, the model efficiently utilizes both confidently labeled and less certain data. At the core of the methodology is the Feature Tokenizer Transformer architecture, which processes both numerical and categorical genomic information. The preprocessing pipeline includes key steps such as data imputation, scaling, and encoding to ensure high data quality. The results highlight the model's impressive accuracy, particularly in detecting confidently labeled variants, while also addressing the impact of its predictions on less certain (soft-labeled) data.
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