Author

Feng Mu

BGI Group (China)ORCID 0000-0002-5708-9013Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:whole-genome sequencing· 3 Organism:human· 2 Research Focus:variant calling· 2 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Xinming LiangiD· 2 shared Andrei Alexeev· 1 shared Ao CheniD· 1 shared Chongjun XuiD· 1 shared Chunyu GengiD· 1 shared Fang CheniD· 1 shared Fang Chen· 1 shared Guangyi FaniD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

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10 top collaborators · 21 mutual co-author links

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2 shared papersBioinformatics

Xinming LiangORCID

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3 papers

Recent·Most cited

BioinformaticsNucleic Acids ResearchPeer-reviewedMay 9, 2022

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution

Meng Yang, Lichao Huang, Haiping Huang…·Feng Mu

LOGO is a lightweight language model for interpreting non-coding regions of the human genome, achieving improved performance in promoter identification and enhancer-promoter interaction prediction.

50 citationstop 5%

humanDNA methylation

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BioinformaticsBMC BioinformaticsPeer-reviewedNov 11, 2020

Detection and characterization of copy number variants based on whole-genome sequencing by DNBSEQ platforms

Junhua Rao, Lihua Peng, Xinming Liang…·Feng Mu

This study evaluates CNV detection performance of DNBSEQ and Illumina platforms using WGS data from NA12878, showing DNBSEQ detects more CNVs with similar sensitivity and precision.

27 citations

humanwhole-genome sequencing

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GenomicsbioRxivPreprintDec 20, 2019

Advanced Whole Genome Sequencing Using an Entirely PCR-free Massively Parallel Sequencing Workflow

Hanjie Shen, Pengjuan Liu, Zhanqing Li…·Radoje Drmanac

This paper demonstrates that PCR-free whole genome sequencing using the DNBSEQ platform and machine learning variant calling significantly reduces InDel errors and improves variant calling performance.

4 citations

whole-genome sequencingmachine learning (classical)

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