Author

Arjun Puranik

Nference (United States)

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 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell type annotation· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

AJ VenkatakrishnaniD· 2 shared Enrique Garcia-RiveraiD· 2 shared Murali Aravamudan· 2 shared Venky SoundararajaniD· 2 shared Abhinav GargiD· 1 shared Agustin Lopez Marquez· 1 shared Ajit Rajasekharan· 1 shared Akash Anand· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersGenomics

AJ VenkatakrishnanORCID

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Author profile →

3 papers

Recent·Most cited

Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

AJ Venkatakrishnan, Arjun Puranik, Akash Anand, David Zemmour, Xiang Yao, Xiaoying Wu, Ramakrishna Chilaka, Dariusz K. Murakowski, Kristopher Standish, Bharathwaj Raghunathan, Tyler Wagner, Enrique Garcia-Rivera, Hugo Solomon, Abhinav Garg, Rakesh D. Barve, , Venky Soundararajan

The paper uses a data science platform to integrate biomedical text and single-cell RNA-seq data to identify new SARS-CoV-2 receptor expression patterns and potential infection targets.

eLife · May 28, 2020Peer-reviewedGenomics

SARS-CoV-2 selectively mimics a cleavable peptide of human ENaC in a strategic hijack of host proteolytic machinery

Praveen Anand, Arjun Puranik, Murali Aravamudan, AJ Venkatakrishnan, Venky Soundararajan

The paper identifies a SARS-CoV-2 peptide that mimics a human ENaC cleavage site, exploiting host proteolytic machinery and linking it to COVID-19 pathophysiology.

eLife · May 26, 2020Peer-reviewedBioinformatics

Recapitulation and Retrospective Prediction of Biomedical Associations Using Temporally-enabled Word Embeddings

Jiho Park, Agustin Lopez Marquez, Arjun Puranik, Ajit Rajasekharan, Murali Aravamudan, Enrique Garcia-Rivera

This paper uses temporally-enabled word embeddings to recapitulate and predict disease-gene associations from OMIM data, achieving high AUC and predicting some associations before their first literature mention.

bioRxiv · May 7, 2019 · PreprintBioinformatics