Author

Nilanjana Laha

Harvard UniversityORCID 0000-0002-1335-2422Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Assay:bulk RNA-seq· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:matrix factorization· 1 Research Focus:polygenic risk· 1

Frequent Co-Authors

Christoph LangeiD· 2 shared Georg HahniD· 2 shared Sharon Marie LutziD· 2 shared Austin Labowitz· 1 shared Diptavo DuttaiD· 1 shared Edwin K. SilvermaniD· 1 shared Michael H. ChoiD· 1 shared Nathan HueyiD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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2 shared papersBioinformatics

Christoph LangeORCID

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Author profile →

4 papers

Recent·Most cited

False Discovery Rate Control for Grouped Hypotheses: Application to miRNAome Data

Nilanjana Laha, Salil Koner, Austin Labowitz, Navonil De Sarkar

This study applies group-adaptive BH methods to miRNAome data, finding they identify more significantly deregulated miRNAs compared to traditional BH, with results supported by prior literature.

PeerJ · May 25, 2026Peer-reviewedBioinformatics

De-biased sparse canonical correlation for identifying cancer-related trans-regulated genes

Nathan Huey, Diptavo Dutta, Nilanjana Laha

The paper introduces a de-biased sparse canonical correlation analysis pipeline to identify cancer-related trans-regulated genes from multi-omic data, finding 256 genes associated with estrogen receptor-related CNAs in breast cancer.

bioRxiv · Aug 15, 2024 · PreprintGenetics

A fast and efficient smoothing approach to Lasso regression and an application in statistical genetics: polygenic risk scores for chronic obstructive pulmonary disease (COPD)

Georg Hahn, Sharon Marie Lutz, Nilanjana Laha, Michael H. Cho, Edwin K. Silverman, Christoph Lange

The paper introduces a smoothed Lasso method for high-dimensional regression and applies it to compute polygenic risk scores for COPD, showing comparable accuracy to FISTA with similar runtime.

Statistics and Computing · Apr 17, 2021Peer-reviewedBioinformatics

A framework to efficiently smooth L1 penalties for linear regression

Georg Hahn, Sharon Marie Lutz, Nilanjana Laha, Christoph Lange

This paper introduces a smoothing framework for L1 penalized regression that provides accurate predictions with theoretical guarantees and comparable runtime to existing methods.

bioRxiv · Sep 17, 2020 · PreprintBioinformatics