利用变压器进行软标签的半监督致病性预测。

IF 1.8 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Pablo Enrique Guillem, Marco Zurdo-Tabernero, Noelia Egido Iglesias, Ángel Canal-Alonso, Liliana Durón Figueroa, Guillermo Hernández, Angélica González-Arrieta, Fernando de la Prieta
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引用次数: 0

摘要

新一代测序(NGS)技术的快速发展彻底改变了基因组学领域,产生了大量数据,需要复杂的分析技术。本文介绍了一个深度学习模型,旨在预测遗传变异的致病性,这是推进个性化医疗的重要组成部分。该模型在NGS输出分析得出的数据集上进行训练,该数据集包含定义良好和模糊的遗传变异的组合。通过采用半监督学习方法,该模型有效地利用了自信标记和不太确定的数据。该方法的核心是Feature Tokenizer Transformer架构,它处理数值和分类基因组信息。预处理流程包括数据输入、缩放和编码等关键步骤,以确保高数据质量。结果突出了该模型令人印象深刻的准确性,特别是在检测自信标记的变体时,同时也解决了其预测对不太确定(软标记)数据的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging transformers for semi-supervised pathogenicity prediction with soft labels.

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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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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