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Articles by Shuting Jin
A Multimodal Dataset of Psychological, Physiological, and Behavioral Responses in Diverse Driving Scenarios
Abstract Driver emotion significantly impacts traffic safety, driving behavior, and driving experience, often being directly influenced by different driving scenarios. To explore this relationship, we present EmoRoad, a multimodal dataset capturing emotional, physiological, and behavioral responses under diverse driving scenarios. These scenarios are defined by three dimensions, each with two conditions: road scenario (urban/suburban), traffic density (jam/flow), and weather (sunny/rainy).
D-Flow: Multi-modality Flow Matching for D-peptide Design
[Submitted on 15 Nov 2024 (v1), last revised 8 Jan 2026 (this version, v3)] View PDF HTML (experimental) Subjects: Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2411.10618 [cs.CE] (or arXiv:2411.10618v3 [cs.CE] for this version) https://doi.org/10.48550/arXiv.2411.10618 Journal reference: NeurIPS 2025 (AI4D3 Workshop)
Modeling the dissemination of privacy information in online social networks
REFERENCES 1, , . Web 1.0 to web 3.0 – evolution of the web and its various challenges. Proc. Int. Conf. Reliab., Optim. Inf. Technol., Faridabad, Haryana, India; Feb. 6–8, 2014:86-89. 2 We Are Social & Hootsuite. Global Digital 2023 Reports. We Are Social Inc.; 2023. 3. Research on the Online Social Network user's Privacy Quantification and Privacy Disclosure Risk in the Information Dissemination, Master's Thesis. Dept. Econ., Beijing Univ. of Posts and Telecommunications; 2017. 4, , , .
FeS Nanosheets Assembled with 1T-MoS2 Nanoflowers on Iron Foam for Efficient Overall Water Splitting
FeS Nanosheets Assembled with 1T-MoS2 Nanoflowers on Iron Foam for Efficient Overall Water Splitting The exploration of highly efficient noble metal-free catalysts for overall water splitting is crucial for industrial application. In this work, FeS nanosheets/1T-MoS2 nanoflowers on conductive iron foam (IF) was prepared as efficient electrocatalysts for both HER and OER by solvothermal method.
A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks
Loading metrics Open Access Peer-reviewed Research Article ? This is an uncorrected proof. Citation: Jin S, Hong Y, Zeng L, Jiang Y, Lin Y, Wei L, et al. (2023) A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks. PLoS Comput Biol 19(11): e1011597.
Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction - Communications Chemistry
Abstract Informative representation of molecules is a crucial prerequisite in AI-driven drug design and discovery. Pharmacophore information including functional groups and chemical reactions can indicate molecular properties, which have not been fully exploited by prior atom-based molecular graph representation. To obtain a more informative representation of molecules for better molecule property prediction, we propose the Pharmacophoric-constrained Heterogeneous Graph Transformer (PharmHGT).
A network-based approach to uncover microRNA-mediated disease comorbidities and potential pathobiological implications
Abstract Disease–disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease–disease relationships by assembling four biological networks: disease–miRNA, miRNA–gene, disease–gene, and the human protein–protein interactome.
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