肿瘤筛查消融研究
CASynergy模型在oncology-screen上的消融研究数据，包含药物分子特征和细胞系基因表达谱，用于评估药物协同预测。
基本信息
资源简介
肿瘤筛查消融研究
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 unique molecular context、improve treatment efficacy、specific molecular interactions、complex biological interactions、attention synergy ),，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Ablation study on oncology-screen.》。源站首次发布：2025-10-15。
【数据内容】
源站原始描述：Cancer drug combination therapies offer a promising strategy to overcome resistance and improve treatment efficacy, but identifying synergistic drug pairs is challenging due to complex biological interactions and tumor heterogeneity. Current machine learning algorithms for drug synergy prediction primarily rely on large-scale, multimodal datasets, yet suffer from critical limitations including poor interpretability, difficulty distinguishing causative biological relationships from correlations,…
中文译文（机器翻译，仅供快速理解，请以原文为准）：癌症药物联合疗法为克服耐药性和提高治疗效果提供了一种有前景的策略，但由于复杂的生物相互作用和肿瘤异质性，识别协同药物对
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 4.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pcbi.1013567.t004。
【主题与分类】
源站学科分类：Biochemistry、Medicine、Genetics、Pharmacology、Biotechnology、Biological Sciences not elsewhere classified。主题标签：unique molecular context、improve treatment efficacy、specific molecular interactions、complex biological interactions、attention synergy ),、two benchmark datasets、provides interpretable insights、personalized cancer treatment、causal attention model、causal attention mechanism。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Ablation_study_on_oncology-screen_/30368538
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Li, Haitao; Zheng, Long; Li, Lei; Chen, Yiwei; Li, Junjie; Zheng, Chunhou; et al. (2025). Ablation study on oncology-screen.. PLOS Computational Biology. Dataset
【使用注意】
· 源站最后修改于 2025-10-23，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Ablation study on oncology-screen.
- DOI：10.1371/journal.pcbi.1013567.t004
- 发布日期：2025-10-15
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Ablation_study_on_oncology-screen_/30368538
命令行下载
curl -L -o "Table 4.xls" "https://ndownloader.figshare.com/files/58773063"
文件清单
Table 4.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Cancer drug combination therapies offer a promising strategy to overcome resistance and improve treatment efficacy, but identifying synergistic drug pairs is challenging due to complex biological interactions and tumor heterogeneity. Current machine learning algorithms for drug synergy prediction primarily rely on large-scale, multimodal datasets, yet suffer from critical limitations including poor interpretability, difficulty distinguishing causative biological relationships from correlations, and inadequate modeling of cancer-specific molecular interactions. To address these challenges, we propose CASynergy (Causal Attention and Cross-attention Synergy), a novel deep learning model for predicting cancer drug synergy that addresses limitations of prior approaches in accuracy and interpretability. CASynergy introduces a causal attention mechanism to distinguish true causal genomic features from spurious correlations, cell line-specific gene network construction to capture the unique molecular context of each cancer cell line, and a cross-attention module to integrate drug molecular features with cell line gene expression profiles. These improvements allow CASynergy to clearly identify significant drug-gene interactions and provides interpretable insights into why a combination is predicted to be synergistic. Experiments on two benchmark datasets (DrugCombDB and Oncology-Screen) suggests that CASynergy outperformed five state-of-the-art models. CASynergy offers a better and more reliable way to predict effective drug combinations. It works well across different cancer types and is easier to understand, which is important for personalized cancer treatment and finding new drugs.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pcbi.1013567.t004（source: https://plos.figshare.com/articles/dataset/Ablation_study_on_oncology-screen_/30368538）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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