Lei Tang
Lei Tang
Madhura Mukhopadhyay
Madhura Mukhopadhyay
Push-button science [0.03%]
一键科学实验
Morgan V Farrell,Emily Darin,Nicholas J Shikuma
Morgan V Farrell
Vivien Marx
Vivien Marx
PANCS-Binders: a rapid, high-throughput binder discovery platform [0.03%]
PANCS-结合分子：一种快速、高通量的结合分子发现平台
Matthew J Styles,Joshua A Pixley,Tongyao Wei et al.
Matthew J Styles et al.
Proteins that selectively bind to a target of interest are foundational in research, diagnostics and therapeutics. Current approaches for discovering binders are laborious and time-consuming, taking months or more, and have a high failure r...
Machine learning-trained protein domain insertion for the design of switchable proteins [0.03%]
基于机器学习的蛋白质结构域插入设计开关型蛋白质
Noah Holzleitner,Julian Grünewald
Noah Holzleitner
Rational engineering of allosteric protein switches by in silico prediction of domain insertion sites [0.03%]
基于计算机预测的领域插入位点的别构蛋白开关的理性工程化研究
Benedict Wolf,Pegi Shehu,Luca Brenker et al.
Benedict Wolf et al.
Domain insertion engineering is a powerful approach to juxtapose otherwise separate biological functions, resulting in proteins with new-to-nature activities. A prominent example are switchable protein variants, created by receptor domain i...
Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines [0.03%]
基于深度学习的基因扰动效应预测尚无法超越简单的线性基准模型
Constantin Ahlmann-Eltze,Wolfgang Huber,Simon Anders
Constantin Ahlmann-Eltze
Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learni...
Zev Kronenberg,Cillian Nolan,David Porubsky et al.
Zev Kronenberg et al.
Recent advances in genome sequencing have improved variant calling in complex regions of the human genome. However, it is difficult to quantify variant calling performance because existing standards often focus on specificity, neglecting co...