Author

Manu Aggarwal

National Institutes of HealthORCID 0000-0002-8262-9341Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:cell type annotation· 2 Assay:scRNA-seq· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Vipul PeriwaliD· 3 shared NG Cogan· 1 shared Xiaoyu DuaniD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

3 top collaborators · 2 mutual co-author links

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3 shared papersSystems Biology

Vipul PeriwalORCID

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3 papers

Recent·Most cited

Mapping Gene Expression to an Interpretable Semantic Space

Xiaoyu Duan, Manu Aggarwal, Vipul Periwal

The paper introduces MESIC, a method that maps gene expression data into an interpretable semantic space using gene annotations, enabling biological interpretation of clusters and outliers.

bioRxiv · Sep 18, 2026 · PreprintBioinformatics

Forecasting drug resistant HIV protease evolution

Manu Aggarwal, Vipul Periwal

The paper develops a probabilistic model to forecast HIV protease drug resistance by simulating evolutionary paths and inferring epistatic interactions from clinical data.

PLoS Computational Biology · Jan 27, 2026Peer-reviewedSystems Biology

Sensitivity based model agnostic scalable explanations of deep learning

Manu Aggarwal, NG Cogan, Vipul Periwal

This paper introduces SensX, an XAI framework that improves explanation accuracy and speed for deep learning models, validated on vision transformers and single-cell RNA-seq data.

bioRxiv · Feb 21, 2025 · PreprintSystems Biology