MDVarP:修饰符 ~ 致病变异对预测器。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-10-08 DOI:10.1186/s13040-024-00392-y
Hong Sun, Yunqin Chen, Liangxiao Ma
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引用次数: 0

摘要

背景:修饰因子通过调节致病变异体的效应对疾病表型产生重大影响,导致个体间疾病表现各不相同。然而,识别修饰基因与致病变异基因之间的遗传相互作用是一项挑战:我们开发了一个由 1000 个随机森林预测因子组成的集合模型 MDVarP,用于识别修饰因子和致病变异体的组合。MDVarP 具有很高的准确性和精确性,这一点已通过一个独立数据集得到验证,该数据集已公布了基因相互作用的证据。我们确定了 25 个新的修饰因子与致病变异体组合,并获得了这些关联的支持性证据。MDVarP 输出了一个类别标签("相关-配对 "或 "非相关-配对")和两个预测分数,这两个分数显示了真正关联的概率:MDVarP 优先考虑与表型调节相关的变异对,从而能更有效地绘制致病变异和调节变异的功能贡献图。该框架解释了人类疾病表型变异背后的基因相互作用,有望应用于个性化医疗和疾病预防。
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MDVarP: modifier ~ disease-causing variant pairs predictor.

Background: Modifiers significantly impact disease phenotypes by modulating the effects of disease-causing variants, resulting in varying disease manifestations among individuals. However, identifying genetic interactions between modifier and disease-causing variants is challenging.

Results: We developed MDVarP, an ensemble model comprising 1000 random forest predictors, to identify modifier ~ disease-causing variant combinations. MDVarP achieves high accuracy and precision, as verified using an independent dataset with published evidence of genetic interactions. We identified 25 novel modifier ~ disease-causing variant combinations and obtained supporting evidence for these associations. MDVarP outputs a class label ("Associated-pair" or "Nonrelevant-pair") and two prediction scores indicating the probability of a true association.

Conclusions: MDVarP prioritizes variant pairs associated with phenotypic modulations, enabling more effective mapping of functional contributions from disease-causing and modifier variants. This framework interprets genetic interactions underlying phenotypic variations in human diseases, with potential applications in personalized medicine and disease prevention.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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