English(EN) AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity prediction

新框架AnnotateMissense预测错义变异致病性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员开发了AnnotateMissense，一个用于预测错义遗传变异致病性的新框架。该系统整合了广泛的数据，包括群体频率、进化保守性和源自AlphaMissense和ESM等蛋白质语言模型的特征。针对超过13万个ClinVar标记变异的基准测试显示，使用303个特征的XGBoost模型取得了0.9411的MCC高绩效。该框架随后应用于超过9000万个变异，以生成致病性评分。 AI

影响为解释遗传变异提供了一个新工具，可能加速基因组学和个性化医疗领域的研究。

排序理由该集群包含一篇详细介绍用于遗传变异解释的新框架和模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍用于遗传变异解释的新框架和模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 128 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Muhammad Muneeb, David B. Ascher · 2026-05-26 04:00

AnnotateMissense：一个用于错义突变致病性预测的全基因组注释和基准测试框架

arXiv:2605.24520v1 Announce Type: cross Abstract: Missense variant interpretation remains challenging because pathogenicity depends on heterogeneous evidence from population frequency, evolutionary conservation, transcript context, amino acid substitution severity, prior pathogen…

本文如何被排名

报道来源 [1]

AnnotateMissense：一个用于错义突变致病性预测的全基因组注释和基准测试框架

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