更正：基于七个免疫相关基因的预后模型预测了肝细胞癌患者的总体生存率。

IF 4 3区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biodata Mining Pub Date : 2023-10-26 DOI:10.1186/s13040-023-00347-9

Qian Yan, Wenjiang Zheng, Boqing Wang, Baoqian Ye, Huiyan Luo, Xinqian Yang, Ping Zhang, Xiongwen Wang

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Correction: A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma.

查看原文本刊更多论文

Correction: A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma.

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来源期刊

Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

7.90

自引率

0.00%

发文量

审稿时长

23 weeks

期刊介绍： BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.