Author

Minh Hoang

Carnegie Mellon UniversityORCID 0000-0002-1053-6744Unclaimed 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· 3 Approach:deep learning· 2 Approach:optimal transport· 2 Assay:whole-genome sequencing· 2 Research Focus:database / resource· 1 Assay:protein sequence· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Carl KingsfordiD· 3 shared C.S. ElderiD· 1 shared Guillaume MarçaisiD· 1 shared Hongyu ZhengiD· 1 shared Isabel Armour-GarbiD· 1 shared Mohsen Ferdosi· 1 shared Mona SinghiD· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 6 mutual co-author links

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3 shared papersBioinformatics

Carl KingsfordORCID

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4 papers

Recent·Most cited

Fast, accurate construction of multiple sequence alignments from protein language embeddings

Minh Hoang, Isabel Armour-Garb, Mona Singh

This paper introduces ARIES, a method using protein language model embeddings to construct multiple sequence alignments, achieving higher accuracy than existing methods, especially in low-identity regimes.

bioRxiv · Mar 13, 2026 · PreprintBioinformatics

A Scalable Optimization Algorithm for Solving the Beltway and Turnpike Problems with Uncertain Measurements

C.S. Elder, Minh Hoang, Mohsen Ferdosi, Carl Kingsford

This paper introduces a scalable optimization algorithm for solving the Beltway and Turnpike problems with uncertain measurements, showing improved accuracy and scalability on synthetic and partial digest data.

Lecture notes in computer science · Jan 1, 2024Peer-reviewedBioinformatics

Masked Minimizers: Unifying sequence sketching methods

Minh Hoang, Guillaume Marçais, Carl Kingsford

This paper introduces masked minimizers, a unifying framework for sequence sketching methods, and proposes a new metric to evaluate and optimize them for better genomic analysis.

bioRxiv · Oct 18, 2022 · PreprintGenomics

DeepMinimizer: A Differentiable Framework for Optimizing Sequence-Specific Minimizer Schemes

Minh Hoang, Hongyu Zheng, Carl Kingsford

The paper introduces a differentiable framework for optimizing sequence-specific minimizer schemes, showing improved performance on genomic sequences.

Lecture notes in computer science · Jan 1, 2022Peer-reviewedBioinformatics