(joint) latent space. Additionally, the generative part of the model provides a way to derive normalized, batch-corrected gene expression and accessibility values for both the multimodal cells (that is, normalizing the observed data) and for unpaired cells (that is, imputing unobserved data; Fig...
Bagal等[34]受生成式预训练新型神经网络模型(generative pre-training transformer,GPT)Transformer在生成文本任务中取得突破性进展的启发,基于GPT构建了一个新的生成模型MolGPT,能够根据给定条件(输入SMILES字符串、脂水分配系数、可合成性分...
Figure 1: A brief evolution of deep generative models over time, measured by model size (number of parameters) and scientific impact (number of citations to date). Three popular deep generative model types are considered: Auto-regressive models (neural language models...
First, the generative model is trained to learn the 3D structural information of known drug-like molecules, with the 3D grid as input structure. 3D grids learn information from three-dimensional conformations. Each grid point stores information about the type of heavy atoms at that point. We ...
To address these issues, we present veloVI (velocity variational inference), a deep generative model for estimating RNA velocity. VeloVI reformulates the inference of RNA velocity via a model that shares information between all cells and genes, while learning the same quantities, namely kinetic par...
Deep learning_CNN_Review:A Survey of the Recent Architectures of Deep Convolutional Neural Networks——2019 CNN综述文章 的翻译 [2019 CVPR] A Survey of the Recent Architectures of Deep Convolutional Neural Networks 翻译 综述深度卷积神经网络架构:从基本组件到结构创新 目录 摘要 1、引言 2、CNN基本组件...
deep learning representation learning deep generative model protein folding protein design Introduction Proteins are linear polymers that fold into various specific conformations to function. The incredible variety of three-dimensional (3D) structures determined by the combination and order in which 20 amino...
generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in ...
a, Distribution of sampling frequencies within a sample of 1 billion SMILES strings from the trained generative model, with known NPSs from the training set shown in red. b, Jensen-Shannon distance between the molecular weights of generated molecules and the set of known NPSs, for molecules gene...
b Model architecture: PRnet is a perturbation-conditioned deep generative model for transcriptional response prediction with three components, including the Perturb-adapter, the Perturb-encoder, and the Perturb-decoder. Crucially, the model operates as a data-driven model, allowing for effective ...