Author

Chang Li

University of South FloridaORCID 0000-0001-9588-8118Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Chang Li2 papers · St. Jude Children's Research Hospital

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:ChIP-seq· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Xiaoming LiuiD· 2 shared Camilla Valente PiresiD· 1 shared Chengqi C. Q. WangiD· 1 shared Degui ZhiiD· 1 shared Jenna OberstalleriD· 1 shared John H. AdamsiD· 1 shared Jun MiaoiD· 1 shared Justin GibbonsiD· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersBioinformatics

Xiaoming LiuORCID

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2 papers

Recent·Most cited

BioinformaticsGenome biologyPeer-reviewedOct 16, 2023

MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites

Chengqi C. Q. Wang, Yibo Dong, Chang Li…·Xiaoming Liu

MalariaSED predicts chromatin profiles in malaria parasites and identifies noncoding variants linked to invasion and drug resistance.

6 citations

ChIP-seqdeep learning

𝕏 PostBluesky

BioinformaticsGenome MedicinePeer-reviewedOct 8, 2022

MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning

Chang Li, Degui Zhi, Kai Wang, Xiaoming Liu

This paper introduces MetaRNN and MetaRNN-indel, deep learning models that differentiate rare pathogenic from benign variants, outperforming existing methods and providing interpretable scores.

195 citationstop 1%

humanwhole-exome sequencing

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