Author

Md Mutasim Billah

Macalester CollegeORCID 0009-0003-2235-8388Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:gene regulatory network inference· 2 Research Focus:GWAS / association mapping· 2 Organism:human· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Fengzhu SuniD· 2 shared Hairong WeiiD· 2 shared Kui ZhangiD· 2 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

3 top collaborators · 3 mutual co-author links

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2 shared papersBioinformatics

Fengzhu SunORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintNov 22, 2025

GBoost-CTL: A novel method in multi-tissue transcriptome-wide associations studies in cross-tissue learner incorporating GWAS information

Md Mutasim Billah, Hairong Wei, Fengzhu Sun, Kui Zhang

GBoost-CTL improves multi-tissue transcriptome-wide association studies by integrating GWAS data and weighting tissues based on predictive accuracy and GWAS signal.

DOI 10.1101/2025.11.22.689782

humanGWAS summary statistics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintOct 25, 2025

TWAS-CTL: A robust and efficient method for multi-tissue transcriptome-wide association studies using cross-tissue learners

Md Mutasim Billah, Hairong Wei, Fengzhu Sun, Kui Zhang

TWAS-CTL improves multi-tissue TWAS by combining single-tissue learners, achieving higher power and efficiency than existing methods.

1 citation

humanbulk RNA-seq

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