Yan C Leyva,Marcelo D T Torres,Carlos A Oliva et al.
Yan C Leyva et al.
Computational protein and peptide design is emerging as a transformative framework for engineering macromolecules with precise structures and functions, offering innovative solutions in medicine, biotechnology and materials science. However...
Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks [0.03%]
单个神经元激活赋予认知任务中涌现回路解决方案的归纳偏差
Pavel Tolmachev,Tatiana A Engel
Pavel Tolmachev
Trained recurrent neural networks (RNNs) have become the leading framework for modelling neural dynamics in the brain, owing to their capacity to mimic how population-level computations arise from interactions among many units with heteroge...
Resolving data bias improves generalization in binding affinity prediction [0.03%]
解决数据偏差可提高结合亲和力预测的泛化性能
David Graber,Peter Stockinger,Fabian Meyer et al.
David Graber et al.
The field of computational drug design requires accurate scoring functions to predict binding affinities for protein-ligand interactions. However, train-test data leakage between the PDBbind database and the Comparative Assessment of Scorin...
Predicting the conformational flexibility of antibody and T cell receptor complementarity-determining regions [0.03%]
抗体和T细胞受体互补决定区构象柔韧性的预测
Fabian C Spoendlin,Monica L Fernández-Quintero,Sai S R Raghavan et al.
Fabian C Spoendlin et al.
Many proteins are highly flexible and their ability to adapt their shape can be fundamental to their functional properties. For example, the flexibility of antibody complementarity-determining region (CDR) loops influences binding affinity ...
Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data [0.03%]
基于单细胞RNA测序数据的监督学习再利用报告：揭示与表型相关的生物学规律
Yingying Cao,Tian-Gen Chang,Sahil Sahni et al.
Yingying Cao et al.
Recent advances in single-cell transcriptome sequencing and computational analysis methods have improved our understanding of cellular heterogeneity. However, associating different cell subsets with phenotypes remains challenging. Recently,...
Error-controlled non-additive interaction discovery in machine learning models [0.03%]
可控的机器学习模型中非加性相互作用的发现算法
Winston Chen,Yifan Jiang,William Stafford Noble et al.
Winston Chen et al.
Machine learning (ML) models are powerful tools for detecting complex patterns, yet their 'black-box' nature limits their interpretability, hindering their use in critical domains like healthcare and finance. Interpretable ML methods aim to...
Conditional generation of real antigen-specific T cell receptor sequences [0.03%]
生成条件下的抗原特异性T细胞受体序列生成
Dhuvarakesh Karthikeyan,Sarah N Bennett,Amy G Reynolds et al.
Dhuvarakesh Karthikeyan et al.
Despite recent advances in T cell receptor (TCR) engineering, designing functional TCRs against arbitrary targets remains challenging due to complex rules governing cross-reactivity and limited paired data. Here we present TCR-TRANSLATE, a ...
Modelling neural coding in the auditory midbrain with high resolution and accuracy [0.03%]
高精度和高分辨率的听觉中脑神经编码建模研究
Fotios Drakopoulos,Lloyd Pellatt,Shievanie Sabesan et al.
Fotios Drakopoulos et al.
Computational models of auditory processing can be valuable tools for research and technology development. Models of the cochlea are highly accurate and widely used, but models of the auditory brain lag far behind in both performance and pe...
Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research [0.03%]
迈向人工智能在生物医学研究中潜在误用风险缓解框架的第一步
Artem A Trotsyuk,Quinn Waeiss,Raina Talwar Bhatia et al.
Artem A Trotsyuk et al.
The rapid advancement of artificial intelligence (AI) in biomedical research presents considerable potential for misuse, including authoritarian surveillance, data misuse, bioweapon development, increase in inequity and abuse of privacy. We...
Mohammad Hosseini,Christopher R Donohue
Mohammad Hosseini