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   Lyu, Zhen; Dahal, Sabin; Zeng, Shuai; Wang, Juexin; Xu, Dong; Joshi, Trupti (July 2024, Genes)

   In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend. 

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2. Mixed Modality Workflows in TaskVine

   https://doi.org/10.1145/3588195.3595953  

   Simonetti, David; Tovar, Ben; Thain, Douglas (August 2023, ACM)

   Modern scientific workflows desire to mix several different comput- ing modalities: self-contained computational tasks, data-intensive transformations, and serverless function calls. To date, these modali- ties have required distinct system architectures with different sched- uling objectives and constraints. In this paper, we describe how TaskVine, a new workflow execution platform, combines these modalities into an execution platform with shared abstractions. We demonstrate results of the system executing a machine learning workflow with combined standalone tasks and serverless functions. 

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3. Understanding Modality Preferences in Search Clarification

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   Kokabi, Mahtab; Rather, Gulam M; Javanmard, Mehdi (August 2025, Biosensors and Bioelectronics: X)
5. Perirhinal damage produces modality-dependent deficits in fear learning

   https://doi.org/10.1016/j.nlm.2021.107427  

   Bartley, Trevor D.; Furtak, Sharon C. (May 2021, Neurobiology of Learning and Memory)

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