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| http://dx.doi.org/10.1002/ajim.23398 | DOI Listing |
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The relationship of spatial visualization ability and number representation: evidence from multiple tasks.
BMC Psychol
January 2025
School of teachers education, Huzhou University, Huzhou, 313000, Zhejiang, China.
It is well established in the literature that the relation of spatial ability and the number representation, but the intrinsic relation of spatial visualization ability and number representation are not well understood. In the Current study, Chinese Preschool children (N = 200; 107 girls; Mage = 5.47years, SD = 0.
View Article and Find Full Text PDFWith the increasing availability of high-quality genome assemblies, pangenome graphs emerged as a new paradigm in the genomics field for identifying, encoding, and presenting genomic variation at both population and species levels. However, it remains challenging to truly dissect and interpret pangenome graphs via biologically informative visualization. To facilitate better exploration and understanding of pangenome graphs towards novel biological insights, here we present a web-based interactive Visualization and interpretation framework for linear-Reference-projected Pangenome Graphs (VRPG).
View Article and Find Full Text PDFCLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction.
Aquat Toxicol
January 2025
School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325001, China. Electronic address:
As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of different chemical substances on different species is a necessary requirement for protecting the environment and ensuring sustainable human development. In this regard, deep learning methods offer significant advantages over traditional experimental approaches in terms of cost, accuracy, and generalization ability.
View Article and Find Full Text PDFUnveiling patterns in spatial transcriptomics data: a novel approach utilizing graph attention autoencoder and multiscale deep subspace clustering network.
Gigascience
January 2025
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
Background: The accurate deciphering of spatial domains, along with the identification of differentially expressed genes and the inference of cellular trajectory based on spatial transcriptomic (ST) data, holds significant potential for enhancing our understanding of tissue organization and biological functions. However, most of spatial clustering methods can neither decipher complex structures in ST data nor entirely employ features embedded in different layers.
Results: This article introduces STMSGAL, a novel framework for analyzing ST data by incorporating graph attention autoencoder and multiscale deep subspace clustering.
EnrichRBP: an automated and interpretable computational platform for predicting and analyzing RNA-binding protein events.
Bioinformatics
January 2025
School of Artificial Intelligence, Jilin University, Jilin, China.
Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.
Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins.
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