Publications

Under Review

Brihadiswaran, G., Matange, K., & Tuck, J. M. Reading Between the Strands: Biophysically Informed Sequence Encodings for Weak-Affinity DNA-DNA Binding Prediction. Bioinformatics (under review).

Brihadiswaran, G.*, Matange, K.*, Tomek, K. J., Volkel, K., Townsend, D., Tuck, J. M., & Keung, A. J. (2026). Deep Learning Predicts Dissimilar DNA-DNA Binding and Engineers Hyperconnected Networks. Nature Communications, 17, 8484.
*Equal contribution
Lee, M. N., Brihadiswaran, G., Rao, B. M., Tuck, J. M., & Keung, A. J. (2025). Yeast Surface Display of Protein Addresses Confers Robust Storage and Access of DNA-Based Data. DNA, 5(3), 34.
Wang, C., Brihadiswaran, G., Jiang, X., & Chattopadhyay, S. (2022). Circ-Tree: A B+-Tree Variant with Circular Design for Persistent Memory. IEEE Transactions on Computers.
Wang, C., Chattopadhyay, S., & Brihadiswaran, G. (2020). Crab-tree: A Crash Recoverable B+-tree Variant for Persistent Memory with ARMv8 Architecture. ACM Transactions on Embedded Computing Systems (TECS).
Haputhanthri, D., Brihadiswaran, G., Gunathilaka, S., Meedeniya, D., Jayarathna, S., Jaime, M., & Harshaw, C. (2020). Integration of Facial Thermography in EEG-based Classification of ASD. International Journal of Automation and Computing, 17, 1-18.
Brihadiswaran, G., Haputhanthri, D., Gunathilaka, S., Meedeniya, D., & Jayarathna, S. (2019). EEG-based processing and classification methodologies for Autism Spectrum Disorder: A Review. Journal of Computer Science , 15(8).

Edippuliarachchi, B., Gamlath, D., Amaratunga, R., Brihadiswaran, G., & Jayasena, S. (2022). Quill: A Memory Efficient k-mer Counting and k-mer Querying Tool for Commodity Clusters. In Proceedings of the 14th International Conference on Bioinformatics and Biomedical Technology (ICBBT ’22) (pp. 79-88).
Brihadiswaran, G. and Jayasena, S. (2021). Frigate: a fast, in-memory tool for counting and querying k-mers. In Proceedings of the 13th International Conference on Bioinformatics and Biomedical Technology (ICBBT ’21) .
Wang, C., Chattopadhyay, S., & Brihadiswaran, G. (2019). Crash recoverable ARMv8-oriented B+-tree for byte-addressable persistent memory. In Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems (pp. 33-44).
Haputhanthri, D., Brihadiswaran, G., Gunathilaka, S., Meedeniya, D., Jayawardena, Y., Jayarathna, S., & Jaime, M. (2019). An EEG based channel optimized classification approach for autism spectrum disorder. In 2019 Moratuwa Engineering Research Conference (MERCon) (pp. 123-128).

Brihadiswaran, G., Matange, K., Tomek, K. J., Volkel, K., Townsend, D., Tuck, J. M., & Keung, A. J. (2026). Deep Learning-Guided DNA-DNA Binding Prediction for Scalable DNA Data Storage. Storage and Computing with DNA, Rome, Italy.

Brihadiswaran, G., Matange, K., & Tuck, J. M. (2026). Predicting Weak-Affinity DNA-DNA Binding with Biophysics-Aware Sequence Encodings. NCSU COE Applied AI Symposium.
Scott, P., Brihadiswaran, G., & Tuck, J. M. (2026). Using Machine Learning to Store Data in DNA More Effectively. ASCEND REU Summer Research Symposium, NC State University.
Matange, K., Brihadiswaran, G., Tomek, K. J., Tuck, J. M., & Keung, A. J. (2025). Advanced Deep Learning Model to Predict DNA-DNA Binding Interactions. Biophysical Journal (Biophysical Society Annual Meeting).
Brihadiswaran, G., Matange, K., Tomek, K. J., Keung, A. J., & Tuck, J. M. (2024). Deep Learning-Driven DNA-DNA Binding Predictions for Efficient DNA-Based Data Storage. NCSU COE Applied AI Symposium.