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Macaque progressions: passing order during single-file movements reflects the social structure of a wild stump-tailed macaque group
Inferring the latent structures of social organisations is a central theme in animal ecology. Sophisticated theoretical frameworks underpin the study...
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GPDRP: a multimodal framework for drug response prediction with graph transformer
BackgroundIn the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often...
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Pan-genome de Bruijn graph using the bidirectional FM-index
BackgroundPan-genome graphs are gaining importance in the field of bioinformatics as data structures to represent and jointly analyze multiple...
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Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network
BackgroundIn cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods...
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Unidirectional single-file transport of full-length proteins through a nanopore
The electrical current blockade of a peptide or protein threading through a nanopore can be used as a fingerprint of the molecule in biosensor...
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A knowledge graph approach to predict and interpret disease-causing gene interactions
BackgroundUnderstanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease...
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A multimodal graph neural network framework for cancer molecular subtype classification
BackgroundThe recent development of high-throughput sequencing has created a large collection of multi-omics data, which enables researchers to...
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Kernelized multiview signed graph learning for single-cell RNA sequencing data
BackgroundCharacterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell...
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A comprehensive benchmark of graph-based genetic variant genoty** algorithms on plant genomes for creating an accurate ensemble pipeline
BackgroundAlthough sequencing technologies have boosted the measurement of the genomic diversity of plant crops, it remains challenging to accurately...
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Exploring Novel Fentanyl Analogues Using a Graph-Based Transformer Model
The structures of fentanyl and its analogues are easy to be modified and few types have been included in database so far, which allow criminals to...
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CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks...
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MMGAT: a graph attention network framework for ATAC-seq motifs finding
BackgroundMotif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of...
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Pangenome graph construction from genome alignments with Minigraph-Cactus
Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a...
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A Graph-Based Mathematical Model for More Efficient Dimensionality Reduction of Landmark Data in Geometric Morphometrics
Geometric Morphometrics can be used to describe morphology as a series of coordinates after the effects of variation in translation, rotation, and...
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Pig pangenome graph reveals functional features of non-reference sequences
BackgroundThe reliance on a solitary linear reference genome has imposed a significant constraint on our comprehensive understanding of genetic...
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DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model
Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations...
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G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics
BackgroundLiquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis...
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GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling
BackgroundProtein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the...
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A graph neural network framework for map** histological topology in oral mucosal tissue
BackgroundHistological feature representation is advantageous for computer aided diagnosis (CAD) and disease classification when using predictive...
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GraphsformerCPI: Graph Transformer for Compound–Protein Interaction Prediction
Accurately predicting compound–protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth...
