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Natarajan Kannan

Blurred image of the arch used as background for stylistic purposes.
Professor, Georgia Cancer Coalition Distinguished Scholar, Institute of Bioinformatics and Department of Biochemistry and Molecular Biology

The Kannan Lab is interested in developing and applying deep learning and machine learning models for bioinformatics applications. Ongoing funded projects are focused on applying large language models for protein function prediction, classification, and text mining. The lab is also interested in representation learning on knowledge graphs, contrastive learning, and generative diffusion models for protein design and dynamics. 

If you are interested in any of these areas, please contact Dr. Kannan ( with a statement of research interest and a CV.

  • Georgia Cancer Coalition Distinguished Cancer Scholar
  • Cold Spring Harbor Laboratory, New York, Postdoc
  • University of California, San Diego, Postdoc
  • Indian Institute of Science, Bangalore, Ph.D., 2001
Selected Publications:
  1. Zhou Z, Yeung W, Soleymani S, Gravel N, Salcedo M, Li S, Kannan N. Using explainable machine learning to uncover the kinase-substrate interaction landscape. Bioinformatics. 2024 Feb 1;40(2):btae033. doi: 10.1093/bioinformatics/btae033. PMID: 38244571; PMCID: PMC10868336
  2. Yeung W, Zhou Z, Li S, Kannan N. Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings. Brief Bioinform. 2023 Jan 19;24(1):bbac599. doi: 10.1093/bib/bbac599. PMID: 36631405; PMCID: PMC9851297.
  3. Taujale R, Zhou Z, Yeung W, Moremen KW, Li S, Kannan N. Mapping the glycosyltransferase fold landscape using interpretable deep learning. Nat Commun. 2021 Sep 27;12(1):5656. doi: 10.1038/s41467-021-25975-9. PMID: 34580305; PMCID: PMC8476585.
  4. Salcedo MV, Gravel N, Keshavarzi A, Huang LC, Kochut KJ, Kannan N. Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding. PeerJ. 2023 Oct 18;11:e15815. doi: 10.7717/peerj.15815. PMID: 37868056; PMCID: PMC10590106.

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