SEARCH · PubMed Health

Results for “Graph”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources

PubMed1992

Analysis of protein sheet topologies by graph theoretical methods.

In order to find rules for the secondary structure prediction of proteins which describe the (sequentially) long-range interactions in sheet structures methods of applied graph theory were used. The so called beta graph which describes the sheet topology was defined for every protein in the Brookhaven Data Bank containing beta sheets. The resemblance of proteins at that topological level is discussed, and four notations and graphic representations of sheets which describe the sequential and topological neighborhoods of the strands were derived. This description level supports the usage of data structures which allow the implementation of efficient algorithms for the analysis and comparison of beta structures in proteins. A computer program for the representation and retrieval of bibliographic data and beta sheet structures was implemented. Some examples for substructure search illustrate the usefulness of the program. Two graphic catalogues were compiled: one contains all beta graphs of PDB proteins and the other all occurring different greek key descriptions.

Computer Simulation↗

PubMed1990

Use of techniques derived from graph theory to compare secondary structure motifs in proteins.

A substructure matching algorithm is described that can be used for the automatic identification of secondary structural motifs in three-dimensional protein structures from the Protein Data Bank. The proteins and motifs are stored for searching as labelled graphs, with the nodes of a graph corresponding to linear representations of helices and strands and the edges to the inter-line angles and distances. A modification of Ullman's subgraph isomorphism algorithm is described that can be used to search these graph representations. Tests with patterns from the protein structure literature demonstrate both the efficiency and the effectiveness of the search procedure, which has been implemented in FORTRAN 77 on a MicroVAX-II system, coupled to the molecular fitting program FRODO on an Evans and Sutherland PS300 graphics system.

Algorithms↗

PubMed1988

Searching for the endogenous benzodiazepine using the graph theoretical approach.

The graph theoretical indices of several compounds with reported benzodiazepine receptor binding affinities were calculated. Our results demonstrate a structural similarity among diazepam, triazolam, and the beta-carboline nucleus and a structural dissimilarity to the purines and nicotinamide. This result correlates with their respective binding affinities. Using the graph theoretical indices as structural descriptors of the benzodiazepines and the significant ligands of the beta-carbolines, a search for peptide sequences as potential ligands was explored. Single amino acids through pentapeptides with all possible amino acid substitutions and chemical modifications were calculated. The peptides generated were subjected to graph theoretical analysis, and their indices were compared to those of the benzodiazepines. Comparisons resulted in seven dipeptides and six tripeptides that are topologically similar to the benzodiazepines and beta-carbolines. The dipeptides are histidine- or tryptophan-containing compounds with pyroglutamine, phenylalanine, and tyrosine residues in the second position. The tripeptides have two aromatic amino acid residues and a pyroglutamine or glycyl terminal residue. These structures are promising candidates because (1) they are structurally (topologically) similar to the benzodiazepines, represented by diazepam and triazolam, and to the beta-carbolines; and (2) they are sequences that may reasonably form a part of a larger peptide or that may be formed metabolically by proteolysis.

Anti-Anxiety Agents↗

PubMed1989

Perceptual and conceptual factors in distortions in memory for graphs and maps.

We propose that representations of visual stimuli are a consequence of both perceptual and conceptual factors that may be revealed in systematic errors in memory. Three experiments demonstrated increased (horizontal or vertical) symmetry in perception and memory of nearly symmetric curves in graphs and rivers in maps. Next, a conceptual factor, an accompanying description biasing toward symmetry or asymmetry, also distorted memory in the expected direction for the symmetric descriptions. In the two final experiments, we investigated conceptual factors in selection of a frame of reference. Subjects remembered lines in graphs, but not in maps, as closer to the imaginary 45 degrees line. Combined with earlier research, this suggests that the reference frame for map lines is the canonical axes and for graph lines, the imaginary 45 degrees line.

Adult↗

PubMed2025

scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.

MOTIVATION: Combining single-cell sequencing with ligand-receptor (LR) analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs naturally represent cell-cell communication events. However, current computational methods cannot yet analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the nonlinear nature of cell-cell communication and the high variability given by the patient-specific single-cell RNAseq datasets. RESULTS: Here, we present scACCorDiON (single-cell Analysis of Cell-Cell Communication in Disease clusters using Optimal transport in Directed Networks), an optimal transport algorithm exploring node distances on the Markov Chain as the ground metric between directed weighted graphs. Benchmarking indicates that scACCorDiON performs a better clustering of samples according to their disease status than competing methods that use undirected graphs. We provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment. Our study case corroborates that clusters provide a robust and explainable representation of cell-cell communication events and that the expression of detected LR pairs is predictive of pancreatic cancer survival. AVAILABILITY AND IMPLEMENTATION: The code of scACCorDiON is available at https://scaccordion.readthedocs.io/en/latest/. and https://doi.org/10.5281/zenodo.15267648. The survival analysis package can be found at https://github.com/CostaLab/scACCorDiON.su.

Humans↗

PubMed2026

PangyPlot: multi-scale interactive visualization of pangenome variation graphs.

SUMMARY: Pangenome variation graphs integrate multiple samples into a unified representation, mitigating the reference bias inherent to linear genomes. However, these graphs can be large and structurally complex. Existing visualization tools are each confined to a fixed scale of resolution, requiring researchers to switch between multiple tools to examine variation at different levels of detail. PangyPlot is an interactive pangenome browser designed for multi-scale exploration of reference variation graphs from full chromosome to nucleotide-level sequence segments. PangyPlot anchors navigation to linear reference coordinates, organizes variation into hierarchical bubble structures, and uses a force-directed layout engine for automatic node arrangement. AVAILABILITY AND IMPLEMENTATION: An instance preloaded with data is available at https://pangyplot.research.sickkids.ca. Source code and documentation are openly available at https://github.com/strug-hub/pangyplot under the MIT License.

Software↗

PubMed2025

Defining and cataloging variants in pangenome graphs.

Structural variation causes some human haplotypes to align poorly with the linear reference genome, leading to 'reference bias'. A pangenome reference graph could ameliorate this bias by relating a sample to multiple reference assemblies. However, this approach requires a new definition of a 'genetic variant.' We introduce a definition of pangenome variants and a method, pantree, to identify them. Our approach involves a pangenome reference tree which includes all nodes (sequences) of the pangenome graph, but only a subset of its edges; non-reference edges are variant edges. Our variants are biallelic and have well-defined positions. Analyzing the Minigraph-Cactus draft human pangenome reference graph, we identified 29.6 million genetic variants. Most variants (99.2%) are small, and most small variants (73.9%) are SNPs. 3.5 million variants (11.7%) have a reference allele which is not on GRCh38; these variants are difficult to detect without a pangenome reference, or with existing pangenome-based approaches. They tend to be embedded within tangled, multiallelic regions. We analyze two medically relevant regions, around the HLA-A and RHD genes, identifying thousands of small variants embedded within several large insertions, deletions, and inversions. We release an open-source software tool together with a VCF variant catalogue.

Journal Article↗

PubMed2026

Graph-based pan-genome reveals structural and functional diversity across oil palm domestication gradients.

BACKGROUND: Oil palm (Elaeis guineensis Jacq.), the world's most land-efficient oil crop, underpins global vegetable oil supply yet faces mounting constraints from limited expansion, climate stress, and disease pressure. These challenges highlight the urgent need for genomic resources that capture species-wide diversity to support sustainable improvement. While recent reference assemblies have advanced trait discovery, single linear genomes fail to represent the full spectrum of structural and gene-content variation, limiting resolution of agronomic alleles. RESULTS: Here, we constructed a graph-based pan-genome from 30 diverse oil palm assemblies representing wild, semi-domesticated, and commercial accessions. We characterized structural variants, gene presence-absence variation, and copy-number gains, with focusing on functional stratification and resistance gene dynamics. The graph-based pan-genome revealed extensive structural and gene-content variation, including a large conserved core, complemented by shell and unique fractions enriched or biased toward regulatory, stress-responsive, and defense-related functions. Structural variation and duplication-derived copy-number gains contributed substantially to gene-content diversity, with semi-domesticated accessions exhibiting the greatest variability. Resistance gene repertoires showed contrasting patterns: receptor-like kinases remained comparatively stable, whereas the CNL subclass of NLR genes contributed disproportionately to shell-genome variation and duplication-associated turnover. CONCLUSIONS: This graph-based pan-genome provides a curated multi-assembly reference and comparative framework for oil palm genomics. By capturing structural variants, gene-content variations, copy-number gains, and resistance gene dynamics across domestication gradients, it establishes a foundation for future pan-GWAS analysis, functional genomics, and molecular breeding strategies aimed at improving resilience and productivity in this globally important crop.

Arecaceae↗

PubMed1975

Linear working graphs in blood lead determinations with the Beckman flameless atomic absorption cuvet.

Working graphs are generally nonlinear when the longitudinal flameless atomic absorption cuvet is used. Such nonlinear curves make it difficult to determine the amount of lead in blood by the method of additions, because of inaccuracies in extrapolating the graph to zero absorbance. This paper describes a method for obtaining linear graphs by making use of opacity (the reciprocal of transmittance, or antilog of the absorbance) instead of absorbance. Because no deuterium arc background corrector is available for the Beckman Model 444, background was correlated by reading the opacity at a nonabsorbing line, 287.3 nm, and subtracting the opacities. A separate reagent-blank correction for lead must also be made.

Adult↗

PubMed1990

Graph-theoretic approach to metabolic pathways.

A graph-theoretic approach is shown to be applicable within the framework of the metabolic control analysis. Kinetic differential equations linearized near a steady state are presented as kinetic graphs (schemes), their structure being correlated with kinetic properties of corresponding metabolic networks. The global properties may be expressed in terms of the local properties for steady states of metabolic systems. Instability, bistability, and concentrational oscillations are shown to be induced by specific graph fragments. The approach is illustrated by an example of systems showing the oscillatory kinetic behaviour.

Biotransformation↗

PubMed1990

Metabolic control theory: a graph-theoretic approach.

The regulatory properties of metabolic pathways are investigated using a graph-theoretic approach. Applying concepts from classical graph theory, analytical expressions are derived for the flux control coefficients of a linear pathway subject to feedback inhibition. It is shown that the relative importance of the various enzymes in the control of flux can be easily established with the aid of this graphical procedure. Another advantage of this technique is that it can be implemented in a step-by-step fashion for simplifying the pathway structure. The graph-theoretic method can also be used for studying the cause-effect relationships of the individual enzymes in a given pathway.

Enzymes↗

PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗

PubMed2026

KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.

BACKGROUND: The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex mechanisms and helps interpret correlative results. RESULTS: The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a findable, accessible, interoperable, reusable and AI-ready knowledge graph (KG). Starting from a core KG with organismal traits, environments, and growth preferences and the integration of established ontologies, the framework generates a hierarchy of related KGs targeting specific use cases, including the human microbiome in the context of disease, or environmental microbiomes. The framework supports customizable taxa subsets representing communities or clades of interest. Evaluations of the KG-Microbe KGs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in studies of inflammatory bowel disease and Parkinson's disease. Finally, the predictive and environmental capabilities of the KGs are demonstrated by predicting growth preferences using graph features. CONCLUSIONS: The KG-Microbe framework unifies microbial contexts in a single resource to support integrative analyses across biomedical, host, and environmental domains. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover candidate mechanistic explanations of microbial associations.

Microbiota↗

PubMed2025

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity.

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity), which uses an equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations to predict drug-target affinity. We augment these representations by incorporating an attention-based mechanism between protein residues and drug atoms to improve interpretability. We show that CASTER-DTA represents a state-of-the-art improvement on multiple benchmarks for predicting drug-target affinity and that it generates novel insights for several related tasks. We then apply CASTER-DTA to create a large resource of the binding affinities of every FDA-approved drug against every protein in the human proteome and make these predictions freely available for download. We also make available a web server for researchers to apply a pretrained CASTER-DTA model for predicting binding affinities between arbitrary proteins and drugs.

deep learning↗

PubMed2026

PheBee: A Graph-Aware System for Scalable, Traceable, and Semantic Phenotyping.

OBJECTIVES: Phenotype-driven workflows in clinical and translational research require standardized ontology-based representation, ontology-aware cohort discovery, and provenance inspection for each assertion. Existing approaches optimize either for semantic traversal or scalable batch analytics, but not both. We describe PheBee, a hybrid system that links semantic assertions to scalable evidence storage via a deterministic identifier, preserving provenance while supporting ontology-aware discovery at cohort scale. MATERIALS AND METHODS: PheBee represents phenotype assertions in a knowledge graph as ontology-linked nodes with clinical modifier context (e.g., negated, family history), and stores supporting evidence records in a scalable row-oriented evidence table for cohort-scale access. The two layers are connected by a deterministic identifier enabling stable joins across repeated ingestions without duplicating high-volume evidence in the graph. We evaluated PheBee using synthetic datasets designed to exercise end-to-end ingestion and query workflows. RESULTS: Functional evaluation validated hierarchical term expansion, qualifier-aware retrieval, duplicate-free assertion handling under re-ingestion, and privacy-conscious management of subjects shared across multiple research projects. At scale (10,000 subjects producing 12M evidence records) PheBee completed ingestion in ~30 minutes and responded to interactive queries within 6 seconds under concurrent load. DISCUSSION: PheBee exposes a unified API for ontology-aware cohort discovery with hierarchical term expansion, subject-centric retrieval of phenotypes and clinical modifiers, and evidence and provenance queries. Its data model aligns with GA4GH Phenopackets, facilitating interoperability with phenotype exchange standards. CONCLUSION: By combining ontology-aware semantics with scalable, provenance-bearing evidence storage, PheBee provides a practical open-source foundation for phenotype-driven research workflows that demand both semantic precision and cohort-scale traceability.

cohort studies↗

PubMed1991

A graph-theoretic approach to modeling metabolic pathways.

The metabolic pathways of medazepam, oxazepam, and diazepam were modeled using graph-theoretic transforms which are incorporable into computer-assisted metabolic analysis programs. The information, represented in the form of a graph-theoretic transform kit, which was obtained from these pathways was then used to predict the metabolites of other benzodiazepine compounds. The transform kits gave statistically significant predictions with respect to a statistical method for evaluating the performance of the transform kits.

Anti-Anxiety Agents↗

PubMed1979

A graph theoretic approach to the development of minimal phylogenetic trees.

The problem of determining the minimal phylogenetic tree is discussed in relation to graph theory. It is shown that this problem is an example of the Steiner problem in graphs which is to connect a set of points by a minimal length network where new points can be added. There is no reported method of solving realistically-sized Steiner problems in reasonable computing time. A heuristic method of approaching the phylogenetic problem is presented, together with a worked example with 7 mammalian cytochrome c sequences. It is shown in this case that the method develops a phylogenetic tree that has the smallest possible number of amino acid replacements. The potential and limitations of the method are discussed. It is stressed that objective methods must be used for comparing different trees. In particular it should be determined how close a given tree is to a mathematically determined lower bound. A theorem is proved which is used to establish a lower bound on the lenghtof any tree and if a tree is found with a length equal to the lower bound, then no shorter tree can exist.

Mathematics↗

PubMed1978

Bond graph models for plant biosystems.

Computable dynamic models for plant biosystems permit the study of effects of environmental variables on plant growth and productivity. Using bond graphs, a comprehensive phenomenological model of a plant biosystem may be developed and used in computer simulations. Elements of a model studied in this papaer include a gas diffusion network between the atmosphere and leaf cytoplasm, intracellular chemistry, and the translocation networks of the phloem. Bond graphs are shown to provide a conceptual basis for the development of biological subsystem and system models and lead to computable representations.

Atmosphere↗