摘要
Robotics&Machine Learning Daily News的新闻记者兼新闻编辑-根据基于预印摘要的新闻报道，我们的记者获得了来自BI orxiv.org的以下引文：“变体效应预测的持续进展对于证明机器学习方法准确确定未知意义变体的临床影响的能力是必要的(VUS)。为实现这一目标，ARSA Critical Assessment of Genome Instruction(CAGI)挑战旨在通过利用219个实验测定的芳基脂肪酶A(ARSA)基因的错义Vu来评估社区提交的变体功能效应预测的性能来描述进展。该挑战涉及15个团队，并评估了来自已建立和最近发布的模型的额外预测。值得注意的是，一个由遗传学和编码训练营参与者开发的模型，经过Python标准机器学习工具的培训，在提交的材料中表现出了卓越的性能。“此外，研究观察到，与不太复杂的技术相比，最先进的深度学习方法在预测性能方面提供了微小但统计学上显著的改善。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: “Continued advances in variant effect prediction are necessary to demonstrate th e ability of machine learning methods to accurately determine the clinical impac t of variants of unknown significance (VUS). Towards this goal, the ARSA Critica l Assessment of Genome Interpretation (CAGI) challenge was designed to character ize progress by utilizing 219 experimentally assayed missense VUS in the Arylsul fatase A (ARSA) gene to assess the performance of community-submitted prediction s of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance am ong submissions. “Furthermore, the study observed that state-of-the-art deep learning methods pro vided small but statistically significant improvement in predictive performance compared to less elaborate techniques.
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