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

IF 4 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

引用次数: 1

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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 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.

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