Mechanistic modeling or machine learning for detecting variants of concern: Why not both?
作者信息
Wilke Claus O, Vieira Luiz C
机构信息
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712.
出版信息
Proc Natl Acad Sci U S A. 2025 Jul 29;122(30):e2513608122. doi: 10.1073/pnas.2513608122. Epub 2025 Jul 21.
Abstract
摘要
相似文献
1
Mechanistic modeling or machine learning for detecting variants of concern: Why not both?用于检测关注变体的机制建模或机器学习：为何不两者兼用？
Proc Natl Acad Sci U S A. 2025 Jul 29;122(30):e2513608122. doi: 10.1073/pnas.2513608122. Epub 2025 Jul 21.
2
Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning.预测奶牛甲烷排放的方法：从传统方法到机器学习。
J Anim Sci. 2024 Jan 3;102. doi: 10.1093/jas/skae219.
3
A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques.一种使用先进特征选择和降维技术检测早期乳腺癌的新型双机器学习方法。
Sci Rep. 2025 Jul 2;15(1):22971. doi: 10.1038/s41598-025-06426-7.
4
The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.生理学与机器学习相结合用于预测阻塞性睡眠呼吸暂停中的持续气道正压通气压力和残余呼吸暂停低通气指数
J Clin Sleep Med. 2025 May 1;21(5):775-782. doi: 10.5664/jcsm.11498.
5
A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study.用于评估、选择和解释2型糖尿病患者心血管疾病结局机器学习模型的责任框架：方法与验证研究
JMIR Med Inform. 2025 Jun 27;13:e66200. doi: 10.2196/66200.
6
Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review.系统生物学中基于机制模型和机器学习方法的结合——系统文献综述。
Comput Methods Programs Biomed. 2023 Oct;240:107681. doi: 10.1016/j.cmpb.2023.107681. Epub 2023 Jun 17.
7
Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.在预测翻修关节成形术方面，机器学习的表现并未优于传统的竞争风险模型。
Clin Orthop Relat Res. 2024 Aug 1;482(8):1472-1482. doi: 10.1097/CORR.0000000000003018. Epub 2024 Mar 12.
8
Performance analysis of machine learning algorithms for the prediction of disinfection byproducts formation during chlorination: Effect of background water characteristics.用于预测氯化过程中消毒副产物形成的机器学习算法性能分析：原水水质特征的影响
J Environ Manage. 2025 Aug;389:126144. doi: 10.1016/j.jenvman.2025.126144. Epub 2025 Jun 14.
9
Quantitative characterisation of extracellular vesicles designed to decoy or compete with SARS-CoV-2 reveals differential mode of action across variants of concern and highlights the diversity of Omicron.旨在与严重急性呼吸综合征冠状病毒2（SARS-CoV-2）诱饵或竞争的细胞外囊泡的定量表征揭示了针对不同关注变体的不同作用模式，并突出了奥密克戎的多样性。
Cell Commun Signal. 2025 Jul 2;23(1):323. doi: 10.1186/s12964-025-02223-x.
10
Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.术前贫血是腰椎融合术后不良结局机器学习预测中一个未被察觉的驱动因素。
Spine J. 2025 Aug;25(8):1596-1607. doi: 10.1016/j.spinee.2025.01.031. Epub 2025 Jan 30.
本文引用的文献
1
Medium-sized protein language models perform well at transfer learning on realistic datasets.中等规模的蛋白质语言模型在真实数据集上的迁移学习中表现良好。
Sci Rep. 2025 Jul 1;15(1):21400. doi: 10.1038/s41598-025-05674-x.
2
Predicting high-fitness viral protein variants with Bayesian active learning and biophysics.利用贝叶斯主动学习和生物物理学预测高适应性病毒蛋白变体
Proc Natl Acad Sci U S A. 2025 Jun 17;122(24):e2503742122. doi: 10.1073/pnas.2503742122. Epub 2025 Jun 9.
3
Active learning-assisted directed evolution.主动学习辅助的定向进化
Nat Commun. 2025 Jan 16;16(1):714. doi: 10.1038/s41467-025-55987-8.
4
Simulating 500 million years of evolution with a language model.用语言模型模拟5亿年的进化历程。
Science. 2025 Feb 21;387(6736):850-858. doi: 10.1126/science.ads0018. Epub 2025 Jan 16.
5
Rapid in silico directed evolution by a protein language model with EVOLVEpro.通过带有EVOLVEpro的蛋白质语言模型进行快速计算机辅助定向进化。
Science. 2025 Jan 24;387(6732):eadr6006. doi: 10.1126/science.adr6006.
6
Biophysical principles predict fitness of SARS-CoV-2 variants.生物物理原理预测 SARS-CoV-2 变体的适应性。
Proc Natl Acad Sci U S A. 2024 Jun 4;121(23):e2314518121. doi: 10.1073/pnas.2314518121. Epub 2024 May 31.
7
Accurate structure prediction of biomolecular interactions with AlphaFold 3.利用 AlphaFold 3 进行生物分子相互作用的精确结构预测。
Nature. 2024 Jun;630(8016):493-500. doi: 10.1038/s41586-024-07487-w. Epub 2024 May 8.
8
Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.机器学习辅助酶工程面临的机遇与挑战
ACS Cent Sci. 2024 Feb 5;10(2):226-241. doi: 10.1021/acscentsci.3c01275. eCollection 2024 Feb 28.
9
Evolutionary-scale prediction of atomic-level protein structure with a language model.用语言模型进行原子级蛋白质结构的进化尺度预测。
Science. 2023 Mar 17;379(6637):1123-1130. doi: 10.1126/science.ade2574. Epub 2023 Mar 16.
10
The landscape of antibody binding affinity in SARS-CoV-2 Omicron BA.1 evolution.SARS-CoV-2 奥密克戎 BA.1 进化过程中抗体结合亲和力的全景。
Elife. 2023 Feb 21;12:e83442. doi: 10.7554/eLife.83442.
Zotero 插件