Author

Lokendra S. Thakur

Harvard University

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

Biology Disciplines

Click to filter author papers

Genetics· 1 Genomics· 1 Microbiology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:machine learning (classical)· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Assay:GWAS summary statistics· 1

Frequent Co-Authors

Gurpreet Bharj· 3 shared Manish SaroyaiD· 1 shared Mengyuan Ding· 1 shared Nilanchali Singh· 1 shared Nino Dekanoidze· 1 shared Sonia Shinde Mahajan· 1 shared Vidhiti Shrivastava· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 12 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersMicrobiology

Gurpreet Bharj

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Rapid and Interpretable AMR Diagnostics via Genomics and Cell Painting using Differential Geometry-based Directed-Simplicial Neural Networks on Multimodal Data

Lokendra S. Thakur, Sonia Shinde Mahajan, Gurpreet Bharj, Mengyuan Ding, Nino Dekanoidze, Vidhiti Shrivastava

This paper develops a multimodal AI model for rapid AMR prediction using genomic and cellular data, identifying key genomic motifs and phenotypic features associated with antimicrobial resistance.

bioRxiv · Mar 12, 2026 · PreprintMicrobiology

MAP-PRS: Multi-Ancestry Portfolio-Based Polygenic Risk Scores

Lokendra S. Thakur, Nilanchali Singh, Gurpreet Bharj

MAP-PRS improves polygenic risk score fairness and reliability across populations by combining ancestry-specific models with portfolio optimization and computational tools.

bioRxiv · Oct 15, 2025 · PreprintGenetics

Interpretable Machine Learning and Comparative Genomics Reveal Microbial Plastic-Degrading (Microbeyt) Potential

Lokendra S. Thakur, Gurpreet Bharj, Manish Saroya

The paper identifies microbial strains with plastic-degrading potential using a computational pipeline combining genomic analysis and interpretable machine learning.

bioRxiv · Sep 17, 2025 · PreprintGenomics