Results 151 to 160 of about 10,447 (245)

Robust Target Association Method with Weighted Bipartite Graph Optimal Matching in Multi-Sensor Fusion. [PDF]

Sensors (Basel)
Wu H, Chen W, Chen W.
europepmc   +1 more source

Orientations of Graphs With at Most One Directed Path Between Every Pair of Vertices

Journal of Graph Theory, Volume 113, Issue 1, Page 143-164, September 2026.
ABSTRACT Given a graph G, we say that an orientation D of G is a KT orientation if, for all u , v ∈ V ( D ), there is at most one directed path (in any direction) between u and v. Graphs that admit such orientations have been used to construct graphs with large chromatic number and small clique number that served as counterexamples to various ...
Barbora Dohnalová, Jiří Kalvoda, Gaurav Kucheriya, Sophie Spirkl   +3 more
wiley   +1 more source

BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions. [PDF]

Brief Bioinform
He S, Wang Z, Li J, Zou Q, Zhang F.
europepmc   +1 more source

Quantitative Spatiotemporal Analysis of Ultrasound Images of Fasciculations in ALS

Muscle &Nerve, Volume 74, Issue 3, Page 644-655, September 2026.
ABSTRACT Introduction/Aims Fasciculations are a hallmark of amyotrophic lateral sclerosis (ALS), yet quantitative description of individual events on muscle ultrasound (MUS) is limited. We characterized the spatiotemporal kinematics of individual fasciculations to determine whether they differ between ALS and other neurogenic conditions.
Ryosuke Sugisawa, Kenji Sekiguchi, Yoshikatsu Noda, Shun Matoba, Hirotomo Suehiro, Jun Kozuki, Tomoko Tanaka, Norio Chihara   +7 more
wiley   +1 more source

Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach. [PDF]

J Am Med Inform Assoc
Miao C, Luo J, Liang Y, Liang H, Cen Y, Guo S, Yu H.   +6 more
europepmc   +1 more source

scMOG: A graph neural network method for regulatory relationship‐preserving single‐cell multi‐omics integration

Quantitative Biology, Volume 14, Issue 3, September 2026.
Abstract Single‐cell multi‐omics sequencing technology provides a powerful tool for studying cellular heterogeneity. However, beyond the challenges of sparsity, heterogeneity, and dimensionality differences, a critical challenge in multi‐omics data integration lies in preserving the true regulatory relationships among molecular features.
Yucheng Lu, Xun Zhang, Hongwei Li
wiley   +1 more source

Maize yield prediction with trait-missing data via bipartite graph neural network. [PDF]

Front Plant Sci
Wang K, Han Y, Zhang Y, Zhang Y, Wang S, Yang F, Liu C, Zhang D, Lu T, Zhang L, Liu Z.   +10 more
europepmc   +1 more source

A method for building a genome-connectome bipartite graph model. [PDF]

J Neurosci Methods, 2019
Yu Q, Chen J, Du Y, Sui J, Damaraju E, Turner JA, van Erp TGM, Macciardi F, Belger A, Ford JM, McEwen S, Mathalon DH, Mueller BA, Preda A, Vaidya J, Pearlson GD, Calhoun VD.   +16 more
europepmc   +1 more source

Genome–phenome association prediction using weighted deep matrix factorization with a multisource graph attention network

Quantitative Biology, Volume 14, Issue 3, September 2026.
Abstract Genome–phenome association (GPA) prediction can broaden the understanding of biological mechanisms underlying complex phenotypic traits (e.g., diseases and agronomic traits). Traditional deep matrix factorization (DMF)‐based GPA methods can integrate multiple data types and uncover nonlinear associations but often rely on low‐dimensional ...
Ran Duan, Wei Cao, Maozu Guo, Le Tian, Lingling Zhao   +4 more
wiley   +1 more source

A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph. [PDF]

BMC Bioinformatics, 2019
Song J, Peng W, Wang F.
europepmc   +1 more source