Xikun Zhang

All Publications

• How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities. ArXiv Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., Califano, A., Cool, J., Dernburg, A. F., Ewing, K., Fox, E. B., Haury, M., Herr, A. E., Horvitz, E., Hsu, P. D., Jain, V., Johnson, G. R., Kalil, T., Kelley, D. R., Kelley, S. O., Kreshuk, A., Mitchison, T., Otte, S., Shendure, J., Sofroniew, N. J., Theis, F., Theodoris, C. V., Upadhyayula, S., Valer, M., Wang, B., Xing, E., Yeung-Levy, S., Zitnik, M., Karaletsos, T., Regev, A., Lundberg, E., Leskovec, J., Quake, S. R. 2024

  Abstract

  The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

  View details for PubMedID 39398201

  View details for PubMedCentralID PMC11468656

• Tools for assembling the cell: Towards the era of cell structural bioinformatics Hu, M., Zhang, X., Latham, A., Sali, A., Ideker, T., Lundberg, E., Hunter, L., Altman, R. B., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2024: 661-665

  Abstract

  Cells consist of large components, such as organelles, that recursively factor into smaller systems, such as condensates and protein complexes, forming a dynamic multi-scale structure of the cell. Recent technological innovations have paved the way for systematic interrogation of subcellular structures, yielding unprecedented insights into their roles and interactions. In this workshop, we discuss progress, challenges, and collaboration to marshal various computational approaches toward assembling an integrated structural map of the human cell.

  View details for Web of Science ID 001258333100051

  View details for PubMedID 38160316

• Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Kumar, S. n., Zhang, X. n., Leskovec, J. n. 2019; 2019: 1269–78

  Abstract

  Modeling sequential interactions between users and items/products is crucial in domains such as e-commerce, social networking, and education. Representation learning presents an attractive opportunity to model the dynamic evolution of users and items, where each user/item can be embedded in a Euclidean space and its evolution can be modeled by an embedding trajectory in this space. However, existing dynamic embedding methods generate embeddings only when users take actions and do not explicitly model the future trajectory of the user/item in the embedding space. Here we propose JODIE, a coupled recurrent neural network model that learns the embedding trajectories of users and items. JODIE employs two recurrent neural networks to update the embedding of a user and an item at every interaction. Crucially, JODIE also models the future embedding trajectory of a user/item. To this end, it introduces a novel projection operator that learns to estimate the embedding of the user at any time in the future. These estimated embeddings are then used to predict future user-item interactions. To make the method scalable, we develop a t-Batch algorithm that creates time-consistent batches and leads to 9× faster training. We conduct six experiments to validate JODIE on two prediction tasks- future interaction prediction and state change prediction-using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by at least 20% in predicting future interactions and 12% in state change prediction.

  View details for DOI 10.1145/3292500.3330895

  View details for PubMedID 31538030

  View details for PubMedCentralID PMC6752886