Author

Nidhi Shah

University of Maryland, College Park

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Research Focus:software / tool· 3 Approach:machine learning (classical)· 2 Research Focus:variant calling· 2 Assay:whole-exome sequencing· 2 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Advait BalajiiD· 1 shared Anthony D. KappelliD· 1 shared Bryce L. KilleiD· 1 shared Daniel J. NaskoiD· 1 shared Dragan PrimoraciD· 1 shared Dreycey AlbiniD· 1 shared Gene D. GodboldiD· 1 shared Héctor Corrada BravoiD· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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1 shared paperBioinformatics

Advait BalajiORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Development and Validation of OncoOrigin: An Integrative AI Tool for Primary Cancer Site Prediction

Petar Brlek, Luka Bulić, Nidhi Shah, Parth Shah, Dragan Primorac

This paper develops OncoOrigin, a machine-learning tool that accurately predicts primary cancer sites from tumor DNA sequencing data in CUP patients, achieving high top-2 accuracy and ROC-AUC.

medRxiv · Nov 29, 2024 · PreprintGenetic & Genomic Medicine

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

Advait Balaji, Bryce L. Kille, Anthony D. Kappell, Gene D. Godbold, Madeline M. Diep, Ryan A. Leo Elworth, Zhiqin Qian, Dreycey Albin, Daniel J. Nasko, Nidhi Shah, Mihai Pop, Santiago Segarra, Krista L. Ternus, Todd J. Treangen

SeqScreen uses ensemble learning to accurately label pathogenic sequences with functional annotations, achieving high recall and precision for microbial pathogenesis.

Genome biology · Jun 20, 2022Peer-reviewedBioinformatics

metagenomeFeatures: An R package for working with 16S rRNA reference databases and marker-gene survey feature data.

Nathan D. Olson, Nidhi Shah, Jayaram Kancherla, Justin Wagner, Joseph Nathaniel Paulson, Héctor Corrada Bravo

The paper introduces an R package for handling 16S rRNA databases and marker-gene survey data, providing consistent interfaces for database comparison and data structure development.

Bioinformatics · Feb 25, 2019Peer-reviewedBioinformatics