A Comprehensive Dataset for lncRNA-Disease-Gene Associations with CGR and FCGR Representations

lncRNA–Disease–Gene Association Dataset with CGR/FCGR Representations DescriptionThis dataset provides a standardized and curated resource for analyzing the associations between long non-coding RNAs (lncRNAs), diseases, and genes. It integrates data from three widely-used biological databases: - LncRNADisease- Comparative Toxicogenomics Database (CTD)- Ensembl It is designed to support applications in bioinformatics, systems biology, and machine learning by combining raw biological sequences, structured association matrices, and visual sequence encodings. --- Dataset Contents - FASTA – 2,594 lncRNA sequences in FASTA format - Associations/ – - 6,662 lncRNA–disease associations - 37,238 gene–disease associations - 256 diseases, 351 genes - Matrices/ – Binary and weighted association matrices for: - lncRNA–disease - gene–disease - Images/ – - CGR (Chaos Game Representation) images - FCGR (Frequency CGR) images - Scripts/ – - scraping.py: Automated extraction from source databases - preprocess.py: Cleaning, normalization, and formatting - generate_images.py: Sequence-to-image transformation (CGR/FCGR) --- Applications This dataset can be used for: - Predictive modeling of lncRNA-disease relationships - Network-based disease association analysis - Sequence-based and image-based machine learning models (e.g., CNNs, transformers) --- ## Reproducibility All scripts and data files are open-access and reproducible. To regenerate the data: python scraping.pypython preprocess.pypython generate_images.py Data Preparation Workflow https://zenodo.org/api/records/15316601/draft/files/data_pipeline_flowchart.png/contentThis diagram shows the six-step data processing pipeline, including extraction, preprocessing, matrix generation, and image transformation.带有混沌游戏表征（Chaos Game Representation, CGR）/频率混沌游戏表征（Frequency CGR, FCGR）的长非编码RNA-疾病-基因关联数据集 ## 描述 本数据集为分析长非编码RNA（long non-coding RNAs, lncRNA）、疾病与基因之间的关联提供了标准化且经人工审编的资源。其整合了三个广泛使用的生物数据库的数据：LncRNADisease、比较毒理基因组学数据库（Comparative Toxicogenomics Database, CTD）以及Ensembl。本数据集旨在结合原始生物序列、结构化关联矩阵与可视化序列编码，为生物信息学、系统生物学与机器学习领域的应用提供支撑。 --- ## 数据集内容 - FASTA 文件：2594条FASTA格式的lncRNA序列 - 关联数据目录： - 6662条lncRNA-疾病关联记录 - 37238条基因-疾病关联记录 - 涵盖256种疾病、351个基因 - 矩阵目录：包含适用于以下场景的二值化与加权关联矩阵： - lncRNA-疾病关联 - 基因-疾病关联 - 图像目录： - CGR（混沌游戏表征）图像 - FCGR（频率混沌游戏表征）图像 - 脚本目录： - scraping.py：用于从源数据库自动提取数据的脚本 - preprocess.py：用于数据清洗、归一化与格式标准化的脚本 - generate_images.py：实现序列到图像转换（CGR/FCGR）的脚本 --- ## 应用场景 本数据集可用于以下研究方向： - lncRNA-疾病关联的预测建模 - 基于网络的疾病关联分析 - 基于序列与图像的机器学习模型（例如卷积神经网络（Convolutional Neural Networks, CNN）、Transformer） --- ## 可复现性说明 所有脚本与数据文件均为开源可复现资源。若需重新生成数据，可执行以下命令： python scraping.py python preprocess.py python generate_images.py 数据准备工作流可参考以下流程图：https://zenodo.org/api/records/15316601/draft/files/data_pipeline_flowchart.png/content，该图展示了包含数据提取、预处理、矩阵生成与图像转换在内的六步数据处理流程。