论文题目 GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images 论文摘要 动机:识别蛋白质亚细胞分布模式和识别癌症组织中的定位生物标记蛋白质对于了解蛋白质功能和相关疾病非常重要。免疫组织化学(IHC)图像可以实现蛋白质在组织水平的分布的可视化,为蛋白...
For evaluating the computational efficiency of our GNN model with respect to CNN models, three different 3D CNN models that have previously been utilized to predict the properties of polycrystalline12or two-phase8,29composite microstructures were trained separately under the same hardware (one Tesla P1...
predict the in-different size.Take Fig.2 for example again,when these twoeak acids are neutralized with the same strong base, the in- teraction label R∈L of an unseen entity pair (GX,GY)new.teraction can be accurately modeled by features of the secondonvolution layer forAcetic acid ...
To address above challenges, we propose a novel Curvature-based Adaptive Graph Neural Network (CurvAGN) for predicting protein-ligand binding affinity. The CurvAGN comprises a curvature block and an adaptive attention guided neural block (AGN). The curvature block assigns edge attributes to include ...
EASSY 1 Temporal Relational Ranking for Stock Prediction This eassy contributed a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. The key novelty of this work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, ...
- Graph level task: In a graph-level task, our goal is to predict the property of an entire graph. For example, for a molecule represented as a graph, we might want to predict what the molecule smells like, or whether it will bind to a receptor implicated in a disease. - Node-lev...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing ...
Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network 图神经网络(GNN)在扩展关系中的应用 RBF神经网络学习算法的研究 毕业论文 图神经网络报告 建模阐述GNN 与GAT等 -Graph Neural Networks (GNN) 《深入浅出图神经网络:GNN原理解析》随笔 图神经网络(GNN)-洞察分析 一种基于...
Proposing a multitask learning framework for learning the feature of each factor by exploiting the dependency and fusing them together to predict the travel time. (iii) Conducting extensive experiments to confirm the effectiveness of our proposed solution in comparison with the state-of-the-art bas...
In drug design, compound potency prediction is a popular machine learning application. Graph neural networks (GNNs) predict ligand affinity from graph representations of protein–ligand interactions typically extracted from X-ray structures. Despite some