表2_组织特异性抗癌肽预测模型比较分析：ACP-Boost框架.csv

数据集获取（Figshare）

• 源站标题：Table 2_Comparative analysis of tissue-specific anticancer peptide prediction models: ACP-Boost framework.csv
• DOI：10.3389/fmolb.2026.1815309.s002
• 发布日期：2026-03-26
• 文件数：1 个
• 文件总体积：19.98 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Comparative_analysis_of_tissue-specific_anticancer_peptide_prediction_models_ACP-Boost_framework_csv/31858351

命令行下载

curl -L -o "Table 2_Comparative analysis of tissue-specific anticancer peptide prediction models: ACP-Boost framework.csv" "https://ndownloader.figshare.com/files/63113737"

文件清单

• Table 2_Comparative analysis of tissue-specific anticancer peptide prediction models: ACP-Boost framework.csv（19.98 KB）

文件总体积 19.98 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Cancer remains a major global health burden, and conventional treatments such as surgery, radiotherapy, and chemotherapy are often limited by systemic toxicity, drug resistance, and high cost. Anticancer peptides (ACPs) have emerged as promising therapeutic candidates because of their selective activity against tumor cells; however, experimental identification of ACPs is labor-intensive and time-consuming, and peptide activity may vary across tissue contexts. Although many computational models have been developed for general ACP prediction, tissue-specific ACP classification remains insufficiently explored. In this study, we developed ACP-Boost, a tissue-aware machine learning framework for tissue-specific ACP classification across nine cancer-related tissues: blood, brain, breast, cervix, colon, liver, lung, prostate, and skin. Experimentally validated peptide records were integrated from CancerPPD2 and DCTPep, followed by preprocessing, redundancy control, and tissue annotation. Peptide sequences were encoded into 473-dimensional feature vectors comprising amino acid composition (AAC), dipeptide composition (DPC), physicochemical property composition (PCP), and pseudo-amino acid composition (PseAAC). To address class imbalance while preserving the original sequence distribution, we formulated the task as a one-versus-rest classification problem, applied group-aware train-test splitting based on peptide sequences, and emphasized evaluation metrics suitable for imbalanced data. We systematically compared five machine learning algorithms, including support vector machine, random forest, logistic regression, k-nearest neighbors, and XGBoost. Among them, XGBoost showed the most stable overall performance across tissues. The results indicate that peptide sequence-derived descriptors contain measurable tissue-associated signals, although predictive separability remains moderate for several cancer types. Feature importance analysis further suggested that both shared charge-related properties and tissue-dependent sequence descriptors contribute to model discrimination. Overall, this study provides a comparative computational framework for tissue-specific ACP classifica

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmolb.2026.1815309.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Comparative_analysis_of_tissue-specific_anticancer_peptide_prediction_models_ACP-Boost_framework_csv/31858351）。