Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data

IF 6.1 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2022-06-11 DOI:10.1186/s13040-022-00297-8
Bin Yang, Wenzheng Bao, Baitong Chen, Dan Song
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Single_cell_GRN:基于监督学习方法和单细胞RNA-seq数据的基因调控网络识别
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
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.
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