Biodata Mining

SCI SCIE

国际简称：BIODATA MIN 参考译名：生物数据挖掘

主要研究方向：MATHEMATICAL & COMPUTATIONAL BIOLOGY 非预警期刊审稿周期：23 Weeks

《生物数据挖掘》(Biodata Mining)是一本由BioMed Central出版的以MATHEMATICAL & COMPUTATIONAL BIOLOGY为研究特色的国际期刊，发表该领域相关的原创研究文章、评论文章和综述文章，及时报道该领域相关理论、实践和应用学科的最新发现，旨在促进该学科领域科学信息的快速交流。该期刊是一本开放期刊，近三年没有被列入预警名单。

3区中科院分区
Q1 JCR分区
91 年发文量
7.9 IF影响因子
开放是否OA
23 H-index
2008 创刊年份
1 issue/year 出版周期
English 出版语言

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.

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Biodata Mining期刊信息

ISSN：1756-0381
出版语言：English
是否OA：开放
E-ISSN：1756-0381
出版地区：ENGLAND
是否预警：否
出版商：BioMed Central
出版周期：1 issue/year
创刊时间：2008
开源占比：0.9896
Gold OA文章占比：100.00%
OA被引用占比：1
出版国人文章占比：0.2
出版撤稿占比：0
研究类文章占比：92.31%

Biodata Mining CiteScore评价数据（2026年6月最新版）

CiteScore

SJR

SNIP

CiteScore 指数

7.2

1.245

1.557

学科类别	分区	排名	百分位
大类：Mathematics 小类：Computational Mathematics	Q1	19 / 212	91%
大类：Mathematics 小类：Computational Theory and Mathematics	Q1	29 / 203	85%
大类：Mathematics 小类：Computer Science Applications	Q1	236 / 1022	76%
大类：Mathematics 小类：Genetics	Q1	84 / 357	76%
大类：Mathematics 小类：Biochemistry	Q2	118 / 448	73%
大类：Mathematics 小类：Molecular Biology	Q2	133 / 414	67%

名词解释：CiteScore 是衡量期刊所发表文献的平均受引用次数，是在 Scopus 中衡量期刊影响力的另一个指标。当年CiteScore 的计算依据是期刊最近4年（含计算年度）的被引次数除以该期刊近四年发表的文献数。例如，2022年的 CiteScore 计算方法为：2022年的 CiteScore =2019-2022年收到的对2019-2022年发表的文件的引用数量÷2019-2022年发布的文献数量注：文献类型包括：文章、评论、会议论文、书籍章节和数据论文。

Biodata Mining中科院评价数据

《新锐期刊分区表》（2026年3月发布）

Top期刊	综述期刊	大类学科		小类学科
否	否	生物学	3区	MATHEMATICAL & COMPUTATIONAL BIOLOGY 数学与计算生物学	2区

Biodata Mining JCR评价数据

2025-2026年最新版

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：MATHEMATICAL & COMPUTATIONAL BIOLOGY	SCIE	Q1	4 / 67	94.8
学科：MATHEMATICAL & COMPUTATIONAL BIOLOGY	SCIE	Q1	6 / 67	91.79

Biodata Mining历年数据统计

影响因子

中科院分区

Biodata Mining中国学者发文选摘

1、BPX-Net: biomarker-preserved explainable networks for disease diagnosis and prognosi
Author: Wang, Jun; Chen, Songchang; Wen, Ru; Ying, Haochao; Xu, Wenqiu; Yin, Lin; Deng, Xiaojuan; Han, Can; Zhu, Qun; Zhang, Bin; Tong, Hongyan; Liu, Chen; Chen, Wei; Jin, Jie; Jin, Kai; Xu, Chenming; Huang, Hefeng; Wang, Huafeng; Qian, Dahong

Journal: BIODATA MINING. 2026; Vol. 19, Issue 1, pp. -. DOI: 10.1186/s13040-026-00537-1
2、Multimodal deep learning for survival prediction and biomarker discovery in non-small cell lung cance
Author: Wang, Yiqing; Xia, Pinghui; Xu, Yang; Xu, Jianxin; Xu, Wenzhen; Lv, Wang; Yan, Junrong; Ou, Qiuxiang; Bao, Hua; Wang, Luming; Hu, Jian

Journal: BIODATA MINING. 2026; Vol. 19, Issue 1, pp. -. DOI: 10.1186/s13040-026-00522-8
3、Early differentiation between paroxysmal and persistent atrial fibrillation based on interpretable machine learning: a multicenter retrospective stud
Author: Li, Sijin; Zhang, Yuqi; Wu, Weijie; Li, Guang; Huang, Tucheng; Zeng, Kuan; Tong, Chao; Li, Heng; Huang, Hui

Journal: BIODATA MINING. 2026; Vol. 19, Issue 1, pp. -. DOI: 10.1186/s13040-026-00525-5
4、An online non-radiographic osteoporosis prediction calculator constructed using interpretable machine learnin
Author: Zhang, Yuqi; Li, Sijin; Mai, Peibiao; Su, Qiang; Gao, Minnan; Zeng, Kuan; Tong, Chao; Zhang, Kun; Huang, Hui

Journal: BIODATA MINING. 2026; Vol. 19, Issue 1, pp. -. DOI: 10.1186/s13040-026-00520-w
5、Construction of an interpretable machine learning model for predicting gestational diabetes mellitus based on 45 dietary nutrient
Author: Xiong, Zhihui; Yuan, Yi; Yun, Zhouhui; Li, Lijie; Chen, Yunmeng

Journal: BIODATA MINING. 2026; Vol. 19, Issue 1, pp. -. DOI: 10.1186/s13040-025-00515-z
6、Recent advances in deep learning for protein-protein interaction: a revie
Author: Cui, Jiafu; Yang, Siqi; Yi, Litai; Xi, Qilemuge; Yang, Dezhi; Zuo, Yongchun

Journal: BIODATA MINING. 2025; Vol. 18, Issue 1, pp. -. DOI: 10.1186/s13040-025-00457-6
7、Enhancing hepatopathy clinical trial efficiency: a secure, large language model-powered pre-screening pipelin
Author: Gui, Xiongbin; Lv, Hanlin; Wang, Xiao; Lv, Longting; Xiao, Yi; Wang, Lei

Journal: BIODATA MINING. 2025; Vol. 18, Issue 1, pp. -. DOI: 10.1186/s13040-025-00458-5
8、Inter-organ correlation based multi-task deep learning model for dynamically predicting functional deterioration in multiple organ systems of ICU patient
Author: Zeng, Zhixuan; Liu, Yang; Yao, Shuo; Lin, Minjie; Cai, Xu; Nan, Wenbin; Xie, Yiyang; Gong, Xun

Journal: BIODATA MINING. 2025; Vol. 18, Issue 1, pp. -. DOI: 10.1186/s13040-025-00445-w