InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining.

Wu, Zhongjia; Wu, Yufeng; Gao, Jingyang

Wu, Zhongjia; Wu, Yufeng; Gao, Jingyang.

Affiliation

Wu Z; College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People's Republic of China.
Wu Y; Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, USA.
Gao J; College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People's Republic of China. gaojy@mail.buct.edu.cn.

BMC Genomics ; 21(Suppl 1): 173, 2020 Mar 05.

Article in En | MEDLINE | ID: mdl-32138660

Subject(s)

Computational Biology/methods; Sequence Analysis, DNA/methods; Sequence Inversion; Data Mining; High-Throughput Nucleotide Sequencing; Internet; Software; Support Vector Machine

Key words

Genomics; High-throughput sequencing; Inversion; Structural variation; Support vector machine

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sequence Analysis, DNA / Computational Biology / Sequence Inversion Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: BMC Genomics Journal subject: GENETICA Year: 2020 Type: Article

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