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.
《生物数据挖掘》是一本开放存取、开放同行评审的期刊,涵盖了数据挖掘应用于高维生物和生物医学数据的所有方面的研究,重点关注从大规模遗传、转录组、基因组、蛋白质组和代谢组数据中发现知识的计算方面。主题领域包括但不限于:-新型数据挖掘和机器学习算法的开发、评估和应用。传统数据挖掘和机器学习算法的适应、评估和应用。-用于数据挖掘和机器学习算法应用的开源软件。设计、开发和集成数据库、软件和网络服务,用于存储、管理、检索和分析大规模研究的数据。-数据挖掘和机器学习结果的预处理、后处理、建模和解释,用于生物解释和知识发现。
年发文量 61
国人发稿量 13.35
国人发文占比 0.22%
自引率 -
平均录取率 -
平均审稿周期 23 Weeks
数据非官方,来自网友分享经验
版面费 US$2390
偏重研究方向 MATHEMATICAL & COMPUTATIONAL BIOLOGY-
期刊官网 https://www.springer.com/journal/13040
投稿链接 https://www.editorialmanager.com/BIDM
Encodings and models for antimicrobial peptide classification for multi-resistant pathogens
来源期刊:BioData Mining DOI:10.1186/s13040-019-0196-x
ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity
来源期刊:BioData Mining DOI:10.1186/s13040-019-0204-1
Disease associations depend on visit type: results from a visit-wide association study
来源期刊:BioData Mining DOI:10.1186/s13040-019-0203-2
Integrative analysis of genetic and epigenetic profiling of lung squamous cell carcinoma (LSCC) patients to identify smoking level relevant biomarkers
来源期刊:BioData Mining DOI:10.1186/s13040-019-0207-y
SEQdata-BEACON: a comprehensive database of sequencing performance and statistical tools for performance evaluation and yield simulation in BGISEQ-500
来源期刊:BioData Mining DOI:10.1186/s13040-019-0209-9
Within-sample co-methylation patterns in normal tissues
来源期刊:BioData Mining DOI:10.1186/s13040-019-0198-8
On the utilization of deep and ensemble learning to detect milk adulteration
来源期刊:BioData Mining DOI:10.1186/s13040-019-0200-5
Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies
来源期刊:BioData Mining DOI:10.1186/s13040-019-0199-7
Predicting metabolite-disease associations based on KATZ model
来源期刊:BioData Mining DOI:10.1186/s13040-019-0206-z
A biplot correlation range for group-wise metabolite selection in mass spectrometry
来源期刊:BioData Mining DOI:10.1186/s13040-019-0191-2
Screening for mouse genes lost in mammals with long lifespans
来源期刊:BioData Mining DOI:10.1186/s13040-019-0208-x
ClickGene: an open cloud-based platform for big pan-cancer data genome-wide association study, visualization and exploration
来源期刊:BioData Mining DOI:10.1186/s13040-019-0202-3
Correction to: Investigating the parameter space of evolutionary algorithms
来源期刊:BioData Mining DOI:10.1186/s13040-019-0210-3
Innovative strategies for annotating the “relationSNP” between variants and molecular phenotypes
来源期刊:BioData Mining DOI:10.1186/s13040-019-0197-9
Characterizing human genomic coevolution in locus-gene regulatory interactions
来源期刊:BioData Mining DOI:10.1186/s13040-019-0195-y
Exploration of a diversity of computational and statistical measures of association for genome-wide genetic studies
来源期刊:BioData Mining DOI:10.1186/s13040-019-0201-4
Testing the assumptions of parametric linear models: the need for biological data mining in disciplines such as human genetics
来源期刊:BioData Mining DOI:10.1186/s13040-019-0194-z
RNSCLC-PRSP software to predict the prognostic risk and survival in patients with resected T1-3N0–2\u2009M0 non-small cell lung cancer
来源期刊:BioData Mining DOI:10.1186/s13040-019-0205-0
Approximate kernel reconstruction for time-varying networks
来源期刊:BioData Mining DOI:10.1186/s13040-019-0192-1