Published in bioRxiv, 2026
Recommended citation: Mittal, E., Litman, E., Myers, T., Agarwal, V., Gopinath, A., & Kassis, T. (2026). MetaMuse: A Multi-Agent AI System for Biomedical Metadata Curation and Harmonization. bioRxiv. https://doi.org/10.64898/2026.04.12.718044 https://doi.org/10.64898/2026.04.12.718044
Published in bioRxiv, 2025
We systematically evaluate TabPFN on high-dimensional RNA-seq classification tasks, identifying key limitations in scalability and performance compared to tree-based ensembles and neural baselines, and provide practical guidance for transcriptomics practitioners.
Recommended citation: Agarwal, V., Li, O., Kassis, T. et al. (2025). Limitations of TabPFN for High-Dimensional RNA-seq Analysis. bioRxiv. https://biorxiv.org
Published in medRxiv, 2025
We develop a mixture-of-experts ensemble clock for transcriptomic age prediction, trained on 57,584 samples spanning 28 tissues (ages 1-114 years). The model achieves R2 = 0.854 and MAE = 4.26 years with calibrated uncertainty intervals, and reveals tissue-specific aging signatures including CDKN2A/p16, AMPD3, MIR29B2CHG, and SEPTIN3.
Recommended citation: Agarwal, V., Li, O., Kassis, T., Gopinath, A. et al. (2025). Transcriptomic Age Prediction Using Mixture-of-Experts Models Reveals Tissue-Specific Aging Signatures. medRxiv. https://medrxiv.org
Published in arXiv, 2025
K-Dense Analyst is an autonomous AI system that performs end-to-end scientific analysis, compressing months of research workflows into hours or days. It combines multi-agent orchestration, automated hypothesis testing, and rigorous statistical validation.
Recommended citation: Agarwal, V., Kassis, T., Li, O., Gopinath, A. et al. (2025). K-Dense Analyst: Towards Fully Automated Scientific Analysis. arXiv. https://arxiv.org
Published in bioRxiv, 2025
Revive-Flow is a foundation model for blood DNA methylation data, trained to capture aging signatures and biological age acceleration. Developed at K-Dense as part of the longevity AI research program.
Recommended citation: Agarwal, V., Li, O., Kassis, T., Gopinath, A. et al. (2025). Revive-Flow: A Foundation Model for Blood DNA Methylation Aging. bioRxiv. https://biorxiv.org
Published in bioRxiv, 2025
GeneJepa is a predictive world model of the transcriptome developed at K-Dense. It learns representations of gene expression states and predicts downstream transcriptomic responses to perturbations.
Recommended citation: Agarwal, V., Li, O., Kassis, T., Gopinath, A. et al. (2025). GeneJepa: A Predictive World Model of the Transcriptome. bioRxiv. https://biorxiv.org
Published in Proceedings of the 24th International Conference on Digital Audio Effects (DAFx20in21), 2021
Recommended citation: Agarwal, V., Cusimano, M., Traer, J., & McDermott, J. H. (2021). Object-based synthesis of scraping and rolling sounds based on non-linear physical constraints. Proceedings of the 24th International Conference on Digital Audio Effects (DAFx20in21), 136–143.