Arunima Singh
Arunima Singh
Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields [0.03%]
用于神经场的在体双光子荧光显微镜的自适应光学校正
Iksung Kang,Hyeonggeon Kim,Ryan Natan et al.
Iksung Kang et al.
Adaptive optics restore ideal imaging performance in complex samples by measuring and correcting optical aberrations but often require custom-built microscopes with carefully aligned wavefront sensing/shaping devices and can be susceptible ...
CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species [0.03%]
CREted：跨组织和物种的基因组和合成细胞类型特异性增强子建模
Niklas Kempynck,Seppe De Winter,Casper H Blaauw et al.
Niklas Kempynck et al.
Sequence-based deep learning models have become the state of the art for analyzing the genomic regulatory code. Particularly for enhancers, these models excel at deciphering sequence grammar that underlies their activity. To enable end-to-e...
AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network [0.03%]
基于预训练神经网络的实验结构测定方法
Alisia Fadini,Minhuan Li,Airlie J McCoy et al.
Alisia Fadini et al.
Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, key challenges persist in modeling side-chain packing, condition-dependent conformational ...
Quantifying uncertainty in protein representations across models and tasks [0.03%]
跨模型和任务下的蛋白质表示不确定性量化
R Prabakaran,Yana Bromberg
R Prabakaran
Biomolecular embeddings serve as efficient representations of sequence and structure, enabling tasks such as similarity searches, structure and function prediction and estimation of biophysical properties. However, relying on embeddings wit...
Gilbert L Henry,Anthony M Zador
Gilbert L Henry