Results 11 to 20 of about 128,486 (353)
Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning [PDF]
Astrophysical Journal, 2019 The subtle and unique imprint of dark matter substructure on extended arcs in strong-lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle ...
J. Brehmer +4 more
semanticscholar +3 more sources
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure [PDF]
Neural Information Processing Systems, 2022 Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by learning the correlation between the input graphs and labels. However, by presenting a graph classification investigation on the training graphs with severe bias, surprisingly, we ...
Shaohua Fan +4 more
semanticscholar +1 more source
Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes
Nature Biotechnology, 2021 Single-cell transcriptomic analysis is widely used to study human tumors. However, it remains challenging to distinguish normal cell types in the tumor microenvironment from malignant cells and to resolve clonal substructure within the tumor.
Ruli Gao +16 more
semanticscholar +1 more source
Nature Communications, 2023 Attempts to explain molecular property predictions of neural networks are not always compatible with chemical intuition based on chemical substructures. Here the authors propose the substructure mask explanation method to tackle this challenge.
Zhenxing Wu +10 more
semanticscholar +1 more source
Substructure Substitution: Structured Data Augmentation for NLP [PDF]
Findings, 2021 We study a family of data augmentation methods, substructure substitution (SUB2), for natural language processing (NLP) tasks. SUB2 generates new examples by substituting substructures (e.g., subtrees or subsequences) with ones with the same label, which
Freda Shi, Karen Livescu, Kevin Gimpel
semanticscholar +1 more source
STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactions [PDF]
Briefings Bioinform., 2021 Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures of drugs
Hui Yu, Shiyu Zhao, Jianyu Shi
semanticscholar +1 more source
Substructure Aware Graph Neural Networks
AAAI Conference on Artificial Intelligence, 2023 Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the ...
DingYi Zeng +5 more
semanticscholar +1 more source
Evidence from the H3 Survey That the Stellar Halo Is Entirely Comprised of Substructure [PDF]
Astrophysical Journal, 2020 In the ΛCDM paradigm, the Galactic stellar halo is predicted to harbor the accreted debris of smaller systems. To identify these systems, the H3 Spectroscopic Survey, combined with Gaia, is gathering 6D phase-space and chemical information in the distant
R. Naidu +7 more
semanticscholar +1 more source
Chemical Science, 2022 Drug–drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that
Ziduo Yang +3 more
semanticscholar +1 more source
Natural product reports (Print), 2021 Covering: up to the end of 2020 Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses.
M. Beniddir +5 more
semanticscholar +1 more source