NCI抗癌活性预测数据
NCI图数据集，化学分子图用于抗癌活性预测的图分类基准。
基本信息
资源简介
该数据集包含化学化合物的分子图表示，用于抗癌活性预测的图分类任务。每个化合物被表示为图，原子为节点，化学键为边，标注为对特定癌症类型是否有活性。数据来源于PubChem，已移除不连通图和异常原子图。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/shiruipan/graph_datasets.git
curl -L -o repo.zip https://github.com/shiruipan/graph_datasets/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
A Repository of Benchmark Graph Datasets for Graph Classification
Introduction to Graph Classification
Recent years have witnessed an increasing number of applications involving objects with structural relationships, including chemical compounds in Bioinformatics, brain networks, image structures, and academic citation networks. For these applications, graph is a natural and powerful tool for modeling and capturing dependency relationships between objects.
Unlike conventional data, where each instance is represented in a feature-value vector format, graphs exhibit node–edge structural relationships and have no natural vector representation. This challenge has motivated many graph classification algorithms in recent years. Given a set of training graphs, each associated with a class label, graph classification aims to learn a model from the training graphs to predict the unseen graphs in future. The following picture shows the difference betweeb classification on vector data and graph data.
Dataset Summaization
This repository maintains 31 benchmark graph datasets, which are widely used for graph classification. The graph datasets consist of:
- chemical compounds
- citation networks
- social networks
- brain networks
The chemical compound graph datasets are in “.sdf” or “.smi” format, and other graph dataset are represented as “.nel” format. All these graph datasets can be handle by frequent subgraph miner packages such as Moss [1] or other softwares. These graphs can be easily converted to other formats handled by Matlab or other softwares.
A summarization of our graph datasets is given in Table 1.
If you used the dataset, please cite the related papers properly.
1. NCI Anti-cancer activity prediction data (NCI)
Description:
The NCI graph datasets are commonly used as the benchmark for graph classification. Each NCI dataset belongs to a bioassay task for anticancer activity prediction, where each chemical compound is represented as a graph, with atoms representing nodes and bonds as edges. A chemical compound is positive if it is active against the corresponding cancer, or negative otherwise. Table 1 summarizes the NCI graph data we download from PubChem. We have removed disconnected graphs and graphs with unexpected atoms (some gra
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 癌症（总论） 的高质量、多模态真实临床数据定制解决方案。
