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| http://dx.doi.org/10.1016/j.gendis.2023.07.001 | DOI Listing |
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Dissecting the cellular architecture and genetic circuitry of the soybean seed.
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Department of Plant Biology, College of Biological Sciences, University of California, Davis, CA 95616.
Seeds are complex structures composed of three regions, embryo, endosperm, and seed coat, with each further divided into subregions that consist of tissues, cell layers, and cell types. Although the seed is well characterized anatomically, much less is known about the genetic circuitry that dictates its spatial complexity. To address this issue, we profiled mRNAs from anatomically distinct seed subregions at several developmental stages.
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While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.
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Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varying graph structures is an important challenge. In this work, we generalize existing spectral clustering algorithms from static to dynamic graphs using canonical correlation analysis to capture the temporal evolution of clusters.
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