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   https://doi.org/10.14778/3641204.3641211  

   Xing, Junjie; Wang, Xinyu; Jagadish, H V (January 2024, Proceedings of the VLDB Endowment)

   Exploratory data analysis can uncover interesting data insights from data. Current methods utilize interestingness measures designed based on system designers' perspectives, thus inherently restricting the insights to their defined scope. These systems, consequently, may not adequately represent a broader range of user interests. Furthermore, most existing approaches that formulate interestingness measure are rule-based, which makes them inevitably brittle and often requires holistic re-design when new user needs are discovered. This paper presents a data-driven technique for deriving an interestingness measure that learns from annotated data. We further develop an innovative annotation algorithm that significantly reduces the annotation cost, and an insight synthesis algorithm based on the Markov Chain Monte Carlo method for efficient discovery of interesting insights. We consolidate these ideas into a system. Our experimental outcomes and user studies demonstrate that DAISY can effectively discover a broad range of interesting insights, thereby substantially advancing the current state-of-the-art. 

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2. Dynamic Multi-Resolution Data Storage

   https://doi.org/10.1145/3352460.3358282  

   Hu, Yu-Ching; Lokhandwala, Murtuza Taher; I., Te; Tseng, Hung-Wei (October 2019, Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture)
3. Multi-View Missing Data Completion

   https://doi.org/10.1109/TKDE.2018.2791607  

   Zhang, Lei; Zhao, Yao; Zhu, Zhenfeng; Shen, Dinggang; Ji, Shuiwang (July 2018, IEEE Transactions on Knowledge and Data Engineering)
4. Multi-site, multi-pollutant atmospheric data analysis using Riemannian geometry

   https://doi.org/10.1016/j.scitotenv.2023.164064  

   Smith, Alexander; Hua, Jinxi; de Foy, Benjamin; Schauer, James J.; Zavala, Victor M. (September 2023, Science of The Total Environment)
5. Explainable multi-task learning for multi-modality biological data analysis

   https://doi.org/10.1038/s41467-023-37477-x  

   Tang, Xin; Zhang, Jiawei; He, Yichun; Zhang, Xinhe; Lin, Zuwan; Partarrieu, Sebastian; Hanna, Emma Bou; Ren, Zhaolin; Shen, Hao; Yang, Yuhong; et al (May 2023, Nature Communications)

   Abstract Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities. 

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