Author

Min Shi

Washington University in St. LouisORCID 0000-0003-4017-5377Unclaimed ORCID profile · follow to get notified of new preprints

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Min Shi3 papers · University of Louisiana at Lafayette

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Assay:ChIP-seq· 1 Research Focus:database / resource· 1 Research Focus:dimensionality reduction· 1 Approach:graph neural network· 1 Research Focus:imputation· 1

Frequent Co-Authors

Shamim Ara MollahiD· 2 shared Maya NatesaniD· 1 shared Mitchell Kong· 1 shared Reetika R. GhagiD· 1 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

4 top collaborators · 6 mutual co-author links

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2 shared papersBioinformatics

Shamim Ara MollahORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 2, 2022

CREWdb 1.0: Optimizing Chromatin Readers, Erasers, and Writers Database using Machine Learning-Based Approach

Maya Natesan, Reetika R. Ghag, Mitchell Kong…·Shamim Ara Mollah

CREWdb 1.0 uses machine learning to predict and classify chromatin regulator proteins, improving the accuracy of a database for readers, erasers, and writers.

3 citations

ChIP-seqmachine learning (classical)

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BioinformaticsbioRxivPreprintJun 5, 2021

NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting

Min Shi, Shamim Ara Mollah

NeTOIF improves time-series omics data imputation and forecasting by integrating network-based methods and LSTM networks, outperforming existing techniques in handling missing values across multiple time points.

1 citation

proteomicsbulk RNA-seq

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