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At least 325 records · Page 18Linked to original sources

PubMed2003

Modeling proteome networks with range-dependent graphs.

In this paper we consider the problem of characterizing and modeling large-scale protein-protein association networks using a class of range-dependent graphs which possess appropriate small world properties. These graphs may be employed in representing given association network using a maximum likelihood approach. This in turn annotates every observed association with its 'range', representing the tendency for such an association to be transitive. The application of a very rapidly developing field of graph theory to the emerging field of proetemics is novel and allows for a many-to-many relationship between individual proteins and groupings of proteins, which in turn may correspond to distinct functional behavior.

Models, Biological↗

PubMed1997

Graphing health status can help pinpoint patient problems.

Diagnosing the cause of a patient's complaints by reviewing physical symptoms alone may overlook indicators of psychological and emotional problems that need to be considered. A health status assessment (see "Health Status Assessment--A Vital Sign for Planning Patient Therapy," pages 2-9) using the Health Status Questionnaire enables a clinician to rapidly sort through the bases of presenting symptoms in physical and psychological terms. After a patient fills out the form, the data are input into a computer, which translates the results into a graph. The physician and patient then use the graph to review the results and develop a plan of therapy. The scenario below demonstrates use of the graph by Northfield, Minn., family practitioner Donald Lum, M.D., along with a standardized, scripted interpretation-and-questions process developed by Atlanta practitioner Dwana Bush, M.D., and Dr. Lum to plan patient therapy.

Adult↗

PubMed1997

Application of fuzzy graph theory to evaluation of human cardiac function.

To explore the possibility of application of the fuzzy graph theory to the evaluation of human cardiac function, the cardiac function of two groups of personnel working under special environment were evaluated using the method of fuzzy graph theory. The first group consists of 31 subjects aged 19-21 years. They were classified according to their cardiac function evaluated by the method of maximum support tree. While the second group consists of 24 subjects aged 30-40 years and were classified with fuzzy graph theory on the basis of 6 maximum principal components extracted from 16 physiological indices. Medical explanation of the results is convincing.

Adult↗

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↗

PubMed2002

The use of information graphs to evaluate and compare diagnostic tests.

OBJECTIVES: The purpose of this communication is to demonstrate the use of "information graphs" as a means of characterizing diagnostic test performance. METHODS: Basic concepts in information theory allow us to quantify diagnostic uncertainty and diagnostic information. Given the probabilities of the diagnoses that can explain a patient's condition, the entropy of that distribution is a measure of our uncertainty about the diagnosis. The relative entropy of the posttest probabilities with respect to the pretest probabilities quantifies the amount of information gained by diagnostic testing. Mutual information is the expected value of relative entropy and, hence, provides a measure of expected diagnostic information. These concepts are used to derive formulas for calculating diagnostic information as a function of pretest probability for a given pair of test operating characteristics. RESULTS: Plots of diagnostic information as a function of pretest probability are constructed to evaluate and compare the performance of three tests commonly used in the diagnosis of coronary artery disease. The graphs illustrate the critical role that the pretest probability plays in determining diagnostic test information. CONCLUSIONS: Information graphs summarize diagnostic test performance and offer a way to evaluate and compare diagnostic tests.

Computer Simulation↗

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↗

PubMed1994

Use of a revised version of the psychotropic medication efficacy graph.

A medication efficacy graph developed by Spirrison and Grosskopf (1991) was revised and applied in a case study to effectively monitor a client's progress. The revision combined several aspects of client behavior into one graph that could aid clinicians in assessing a client under psychiatric care. Revised applications of the graph should also enable clinicians to measure and display not just incidents of maladaptive behaviors but weighted maladaptive behaviors, adaptive behaviors, potency of medication, and side effects, all of which provide a better overall picture of the client's behavior.

Adult↗

PubMed1993

Programme for drawing line graphs on IBM personal computers.

A simple program for drawing line graphs on IBM Personal Computers is described here. This program is written in Basic language and is user friendly. This program allows the operator to plot the line graphs with standard error of each of the observations. After plotting suitable legend can also be added at appropriate places in the graph. In the graphic mode a hard copy can be obtained from a dot matrix printer using print screen command.

Microcomputers↗

PubMed1996

Superposition of sphero-cylindrical lenses using contour graphs.

BACKGROUND: The superposition of thin sphero-cylindrical lenses with arbitrary angles between cylindrical axes is sometimes required in a clinical setting. For example, it may be useful in some instances to perform an overrefraction which, added to the spectacle or toric contact lens in place on the eye, yields a final correcting lens. METHODS: Computer calculations have been used to create contour graphs allowing a graphical solution to the problem of combining sphero-cylindrical lenses. Although conceptually strightforward, the method has not been presented before. RESULTS: The use of contour graphs to superpose sphero-cylindrical lenses is shown by examples to be accurate and much simpler than calculational methods. CONCLUSIONS: Lacking a pre-programmed computer, the method of contour graphs offers a practical method of combining sphero-cylindrical lenses.

Eyeglasses↗

PubMed1997

Single bone straight line graphs for the lower extremity.

Valuable information about growth and growth prediction in the lower extremity has been provided in the past by Anderson and Green, Moseley, and Menelans. Greater patient expectations and advanced techniques for lower extremity lengthening require more precise information regarding the growth characteristics of each long bone. A simple method for predicting growth is presented for the femur and tibia. Straight line graphs similar to one described previously by Moseley for the entire lower extremity have been drawn separately for the femur and tibia. Information from a contemporary population and new knowledge about growth plate activity have been included to provide more accurate predictions. By superimposing identically scaled growth remaining graphs on these single bone straight line graphs, a simple and accurate estimate is obtained for the timing and effect of epiphysiodeses or lengthening procedures.

Adolescent↗

PubMed1998

Conceptual graph grammar--a simple formalism for sublanguage.

There are a wide variety of computer applications that deal with various aspects of medical language: concept representation, controlled vocabulary, natural language processing, and information retrieval. While technical and theoretical methods appear to differ, all approaches investigate different aspects of the same phenomenon: medical sublanguage. This paper surveys the properties of medical sublanguage from a formal perspective, based on detailed analyses cited in the literature. A review of several computer systems based on sublanguage approaches shows some of the difficulties in addressing the interaction between the syntactic and semantic aspects of sublanguage. A formalism called Conceptual Graph Grammar is presented that attempts to combine both syntax and semantics into a single notation by extending standard Conceptual Graph notation. Examples from the domain of pathology diagnoses are provided to illustrate the use of this formalism in medical language analysis. The strengths and weaknesses of the approach are then considered. Conceptual Graph Grammar is an attempt to synthesize the common properties of different approaches to sublanguage into a single formalism, and to begin to define a common foundation for language-related research in medical informatics.

Computer Graphics↗

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↗

PubMed1996

Regret graphs, diagnostic uncertainty and Youden's Index.

Regret is the difference in outcome between the action we took and the best action we could, in retrospect, have taken. 'Tent graphs', representing decision problems under uncertainty in terms of expected regret, offer an instructive geometric supplement to standard formulae, allow instant calculations, and suggest, as we shall illustrate, novel algebraic properties. For instance, the problem of finding the pre-test probability range in which it is worthwhile doing a diagnostic test becomes trivial, even when test costs depend on the unknown disease status; we take the opportunity to explore the (non-trivial) algebraic properties of this problem. The graphs also provide a simple way to illustrate and read off the expected value of information and expected value of perfect information. This property is used to derive a clinical interpretation of Youden's Index (sensitivity + specificity - 1), namely, it is the maximum proportional reduction in expected regret achieved by the test. Finally, we examine the relationship between Youden's Index and the area under an ROC curve.

Aged↗

PubMed1998

A statistical method for quantitative evaluation of the progression of chronic diseases: the mean score graph (MSG).

We present a quantitative method of evaluation of the clinical course of chronic disease with long-term progressive deterioration. The method takes into account information on all patients, whatever their duration of follow-up. We present the 'mean score graph' as a descriptive device which is an extension of a survival graph. The description of progression of idiopathic torsion dystonia, comparing progression in males and females, is used as an example of the application. A test for group comparison is described.

Age of Onset↗