Author

Rajendra KC

ORCID 0000-0002-6403-8919Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Molecular Biology· 2 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Research Focus:benchmarking· 1 Assay:CRISPR screen· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1

Frequent Co-Authors

Kirsten A. FairfaxiD· 3 shared Alex William HewittiD· 2 shared Alexander J. BarnettiD· 1 shared Andrew S. FliesiD· 1 shared Anuk KruawaniD· 1 shared Chrissie E. B. OngiD· 1 shared David A GelliD· 1 shared Heshadi Primrose Primrose MandalawattaiD· 1 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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3 shared papersMolecular Biology

Kirsten A. FairfaxORCID

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3 papers

Recent·Most cited

Development of a point-of-care field diagnostic test for DFT1 and DFT2

Anuk Kruawan, K.C. Rajendra, David A Gell, Jocelyn M. Darby, Weizhen Zhu, Chrissie E. B. Ong, Kirsten A. Fairfax, Andrew S. Flies

This paper develops a field-deployable diagnostic test for DFT1 and DFT2 using SHERLOCK to detect DNA in attomolar range and non-invasive samples.

bioRxiv · Dec 19, 2025 · PreprintMolecular Biology

Exploring Prime Editing strategies for genetic correction of a Ceroid Neuronal Lipofuscinosis type 2 disease causing variant

Heshadi Primrose Primrose Mandalawatta, Rajendra KC, Kirsten A. Fairfax, Alex William Hewitt

This study uses prime editing to correct a common TPP1 mutation in cultured cells and eVLPs, achieving on-target editing efficiencies of up to 27.36% and 12.6% in vivo.

bioRxiv · Oct 2, 2025 · PreprintMolecular Biology

Systematic comparison of Generative AI-Protein Models reveals fundamental differences between structural and sequence-based approaches.

Alexander J. Barnett, Rajendra KC, Pratikshya Pandey, Pamodha Somasiri, Kirsten A. Fairfax, Alex William Hewitt

This study compares generative AI models for protein design, finding structural models produce more confident structures but less diversity, while language models generate more diverse proteins with lower structural confidence.

bioRxiv · Mar 23, 2025 · PreprintSynthetic Biology