Start 2026 with deep learning and agentic AI analysis of single-cell RNAseq data.
Date and Time: Friday 30th January 2026 at 3.30pm.
Place: School of Computing Science, Sir Alwyn Williams Building, Room 423/424.
Talk 1: Multi-agent AI enables evidence-based cell annotation in single-cell transcriptomics Dr Parashar Dhapola, Co-founder and CEO of Nygen Analytics, Lund, Sweden.https://www.nygen.io/
Associated research papers:[1] @Gautam Ahuja, Alex Antill, Yi Su, Giovanni Marco Dall'Olio’Olio, Sukhitha Basnayake, Göran Karlsson, Parashar Dhapola (2025) Multi-agent AI enables evidence-based cell annotation in single-cell transcriptomics, bioRxiv 2025.11.06.686964https://lnkd.in/d3vAvZjh
Talk 2: Deciphering complex biological systems using AI - from generative models to language models Dr Cen Wan (Senior Lecturer in Bioinformatics, School of Computing and Mathematical Sciences, Birkbeck College, University of London)
Associated research papers:[1] Alsaggaf, I., Buchan, D. and Wan, C. (2025) Less is more: Improving cell-type identification with augmentation-free single-cell RNA-Seq contrastive learning, Bioinformatics, btaf437.https://lnkd.in/eEVcd5gu[2] Alsaggaf, I., Buchan, D. and Wan, C. (2025) An extensive evaluation of single-cell RNA-Seq contrastive learning generative networks for intrinsic cell-types distribution estimation, bioRxiv. DOI: 10.1101/2025.09.15.675691.https://lnkd.in/e5aWAF8V[3] Rafi, A., et al. (2024) A community effort to optimize sequence-based deep learning models of gene regulation, Nature Biotechnology.https://lnkd.in/et2DvPrP
Thanks to Kevin Bryson for organising this session.