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At least 91 records · Page 5Linked to original sources
PubMed1991

Knowledge-based simulation of DNA metabolism: prediction of enzyme action.

We have developed a knowledge-based simulation of DNA metabolism that accurately predicts the actions of enzymes on DNA under a large number of environmental conditions. Previous simulations of enzyme systems rely predominantly on mathematical models. We use a frame-based representation to model enzymes, substrates and conditions. Interactions between these objects are expressed using production rules and an underlying truth maintenance system. The system performs rapid inference and can explain its reasoning. A graphical interface provides access to all elements of the simulation, including object representations and explanation graphs. Predicting enzyme action is the first step in the development of a large knowledge base to envision the metabolic pathways of DNA replication and repair.

Adenosine Monophosphate↗
PubMedDate not supplied

Comparison of parzen density and frequency histogram as estimators of probability density functions.

In neurobiology, and in other fields, the frequency histogram is a traditional tool for determining the probability density function (pdf) of random processes, although other methods have been shown to be more efficient as their estimators. In this study, the frequency histogram is compared with the Parzen density estimator, a method that consists of convolving each measurement with a weighting function of choice (Gaussian, rectangular, etc) and using their sum as an estimate of the pdf of the random process. The difference in their performance in evaluating two types of pdfs that occur commonly in quantal analysis (monomodal and multimodal with equidistant peaks) is demonstrated numerically by using the integrated square error criterion and assuming a knowledge of the "true" pdf. The error of the Parzen density estimates decreases faster as a function of the number of observations than that of the frequency histogram, indicating that they are asymptotically more efficient. A variety of "reasonable" weighting functions can provide similarly efficient Parzen density estimates, but their efficiency greatly depends on their width. The optimal widths determined using the integrated square error criterion, the harmonic analysis (applicable only to multimodal pdfs with equidistant peaks), and the "test graphs" (the graphs of the second derivatives of the Parzen density estimates that do not assume a knowledge of the "true" pdf, but depend on the distinction between the "essential features" of the pdf and the "random fluctuations") were compared and found to be similar.

Fourier Analysis↗
PubMed2003

Mixed Markov models.

Markov random fields can encode complex probabilistic relationships involving multiple variables and admit efficient procedures for probabilistic inference. However, from a knowledge engineering point of view, these models suffer from a serious limitation. The graph of a Markov field must connect all pairs of variables that are conditionally dependent even for a single choice of values of the other variables. This makes it hard to encode interactions that occur only in a certain context and are absent in all others. Furthermore, the requirement that two variables be connected unless always conditionally independent may lead to excessively dense graphs, obscuring the independencies present among the variables and leading to computationally prohibitive inference algorithms. Mumford [Mumford, D. (1996) in ICIAM 95, eds. Kirchgassner, K., Marenholtz, O. & Mennicken, R. (Akademie Verlag, Berlin), pp. -->233-256-->] proposed an alternative modeling framework where the graph need not be rigid and completely determined a priori. Mixed Markov models contain node-valued random variables that, when instantiated, augment the graph by a set of transient edges. A single joint probability distribution relates the values of regular and node-valued variables. In this article, we study the analytical and computational properties of mixed Markov models. In particular, we show that positive mixed models have a local Markov property that is equivalent to their global factorization. We also describe a computationally efficient procedure for answering probabilistic queries in mixed Markov models.

Journal Article↗
PubMed2003

Common Structural Cliques: a tool for protein structure and function analysis.

Proposed is a method for locating functionally relevant atoms in protein structures and a representation of spatial arrangements of these atoms allowing for a flexible description of active sites in proteins. The search method is based on comparison of local structure features of proteins that share a common biochemical function. The method does not depend on overall similarity of structures and sequences of compared proteins or on previous knowledge about functionally relevant residues. The compared protein structures are condensed to a graph representation, with atoms as nodes and distances as edge labels. Protein graphs are then compared to extract all possible Common Structural Cliques. These cliques are merged to create Structural Templates: graphs that describe structural analogies between compared proteins. Structures of serine endopeptidases were compared in pairs using the presented algorithm with different geometrical parameters. Additionally, a Structural Template was extracted from the structures of aminotransferases, two different proteins that catalyze the same type of chemical reaction. The results presented show that the method works efficiently even in the case of large protein systems and allows for extraction of common structural features from proteins catalyzing a particular chemical reaction, but that evolved from different ancestors by convergent evolution.

Algorithms↗
PubMed2006

H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge.

The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The 'Rosetta compendium'). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge.

Algorithms↗
PubMed2004

FREX: a query interface for biological processes with hierarchical and recursive structures.

An intelligent system for signal transduction pathways and other higher order functional knowledge is presented. Molecular mechanisms of biological processes are typically represented as diagrams ("pathways") that have a graph-analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and range from metal ion to protein to biological processes in general. In addition, the kinds of interactions that connect biological entities are likewise diverse. Consequently, current knowledge about pathways is highly heterogeneous both in the sense of the types of constituents and the granularity of descriptions. To cope with this problem, the proposed system adopts a recursive and hierarchical representation model that enables the annotation and query of pathways or sub-pathways of arbitral granularity. By combining the use of this hierarchical structure and biological ontologies, literature-based information regarding biological mechanisms becomes accessible by computer.

Computational Biology↗
PubMed2005

GIS analysis for the marine environmental data off Karnataka coast.

Geographic Information System (GIS) is used to develop the Oceanographic Information System (OIS) for marine environmental data off Karnataka coast. Its goal is to provide a set of tools for oceanographic and bathymetry data assessment and products like graphs, query system for the users. The most significant interfaces are the GIS based Map-query and Contour visualization. The interfaces are user-friendly and oriented towards bridging the gap between user's knowledge and the technical knowledge required to operate the software system. The OIS also provides users with other analysis tools like query based reports and graphs. OIS can be used as a powerful tool to synthesize not only all the data off Karnataka coast but for comprehensive interpretation and forecasting of marine environmental ecosystems for any region. This system has been applied to the Karnataka Coastal Environment, India and it is very useful for the clients and policy makers.

Chlorophyll↗
PubMed1996

A graph-grammar approach to represent causal, temporal and other contexts in an oncological patient record.

The data of a patient undergoing complex diagnostic and therapeutic procedures do not only form a simple chronology of events, but are closely related in many ways. Such data contexts include causal or temporal relationships, they express inconsistencies and revision processes, or describe patient-specific heuristics. The knowledge of data contexts supports the retrospective understanding of the medical decision-making process and is a valuable base for further treatment. Conventional data models usually neglect the problem of context knowledge, or simply use free text which is not processed by the program. In connection with the development of the knowledge-based system THEMPO (Therapy Management in Pediatric Oncology), which supports therapy and monitoring in pediatric oncology, a graph-grammar approach has been used to design and implement a graph-oriented patient model which allows the representation of non-trivial (causal, temporal, etc.) clinical contexts. For context acquisition a mouse-based tool has been developed allowing the physician to specify contexts in a comfortable graphical manner. Furthermore, the retrieval of contexts is realized with graphical tools as well.

Adverse Drug Reaction Reporting Systems↗
PubMed2004

The KEGG resource for deciphering the genome.

A grand challenge in the post-genomic era is a complete computer representation of the cell and the organism, which will enable computational prediction of higher-level complexity of cellular processes and organism behavior from genomic information. Toward this end we have been developing a knowledge-based approach for network prediction, which is to predict, given a complete set of genes in the genome, the protein interaction networks that are responsible for various cellular processes. KEGG at http://www.genome.ad.jp/kegg/ is the reference knowledge base that integrates current knowledge on molecular interaction networks such as pathways and complexes (PATHWAY database), information about genes and proteins generated by genome projects (GENES/SSDB/KO databases) and information about biochemical compounds and reactions (COMPOUND/GLYCAN/REACTION databases). These three types of database actually represent three graph objects, called the protein network, the gene universe and the chemical universe. New efforts are being made to abstract knowledge, both computationally and manually, about ortholog clusters in the KO (KEGG Orthology) database, and to collect and analyze carbohydrate structures in the GLYCAN database.

Animals↗
PubMed2006

Communicating population health statistics through graphs: a randomised controlled trial of graph design interventions.

BACKGROUND: Australian epidemiologists have recognised that lay readers have difficulty understanding statistical graphs in reports on population health. This study aimed to provide evidence for graph design improvements that increase comprehension by non-experts. METHODS: This was a double-blind, randomised, controlled trial of graph-design interventions, conducted as a postal survey. Control and intervention participants were randomly selected from telephone directories of health system employees. Eligible participants were on duty at the listed location during the study period. Controls received a booklet of 12 graphs from original publications, and intervention participants received a booklet of the same graphs with design modifications. A questionnaire with 39 interpretation tasks was included with the booklet. Interventions were assessed using the ratio of the prevalence of correct responses given by the intervention group to those given by the control group for each task. RESULTS: The response rate from 543 eligible participants (261 intervention and 282 control) was 67%. The prevalence of correct answers in the control group ranged from 13% for a task requiring knowledge of an acronym to 97% for a task identifying the largest category in a pie chart. Interventions producing the greatest improvement in comprehension were: changing a pie chart to a bar graph (3.6-fold increase in correct point reading), changing the y axis of a graph so that the upward direction represented an increase (2.9-fold increase in correct judgement of trend direction), a footnote to explain an acronym (2.5-fold increase in knowledge of the acronym), and matching the y axis range of two adjacent graphs (two-fold increase in correct comparison of the relative difference in prevalence between two population subgroups). CONCLUSION: Profound population health messages can be lost through use of overly technical language and unfamiliar statistical measures. In our study, most participants did not understand age standardisation and confidence intervals. Inventive approaches are required to address this problem.

Adult↗
PubMed2006

A probabilistic methodology for integrating knowledge and experiments on biological networks.

Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, and systematically using these large datasets for refining existing knowledge is a major challenge. Here we introduce an extended computational framework that combines formalization of existing qualitative models, probabilistic modeling, and integration of high-throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge, and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph, and the framework accommodates partial measurements of diverse biological elements. We study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using hypothesis testing and derive p-values for learned model features. We test our methodology and algorithms on a simulated model and on two real yeast models. In particular, we use our method to explore uncharacterized relations among regulators in the yeast response to hyper-osmotic shock and in the yeast lysine biosynthesis system. Our integrative approach to the analysis of biological regulation is demonstrated to synergistically combine qualitative and quantitative evidence into concrete biological predictions.

Cell Physiological Phenomena↗
PubMed1980

A simple objective system for early recognition of overwhelming neonatal respiratory distress.

The early recognition of severe respiratory distress in the newborn allows for optimal care. The prompt identification of overwhelming respiratory failure may assist in the selection of candidates for new therapeutic techniques. We have evaluated, both retrospectively and prospectively, a simple scoring system for neonatal respiratory insufficiency. During the first 24 hr of life, serial inspired oxygen values (FiO2) are plotted with serial pH measurements against time on a graph. With pulmonary insufficiency, the lines cross. The severity of the insufficiency is quantified by integrating the area between the crossed lines. The 25 infants in our Regional Intensive Care Unit Nursery who died with respiratory distress syndrome (RDS) during 1976 were compared with surviving infants matched for gestational age, birth weight, and admission date; all patients received similar conventional management. The difference in the mean 24-hr cumulative scores between the two groups was significant (p < 0.01). Only 1 infant with a score over 40 U ultimately survived (96% specificity). From January 1978 through June 1979, data were graphed at the bedside on 100 neonates who required respiratory support, and analyzed without knowledge of the eventual outcome. Overall, the scoring system predicted the final outcome in 95% of the cases. False positive determinations were minimal, the system accurately selecting 86/87 ultimate survivors (98.8% specificity). These data suggest that this simple scoring system may prove useful in identifying infants with overwhelming respiratory distress. Such infants may be considered for specialized care or innovative yet unproven treatment modalities.

Humans↗
PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. RESULTS: We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g. associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell composition. Our MPAC R package enables similar multi-omic analyses on new datasets. AVAILABILITY AND IMPLEMENTATION: The MPAC package is available at Bioconductor https://bioconductor.org/packages/MPAC.

Humans↗
PubMed2006

Assessing semantic similarity measures for the characterization of human regulatory pathways.

MOTIVATION: Pathway modeling requires the integration of multiple data including prior knowledge. In this study, we quantitatively assess the application of Gene Ontology (GO)-derived similarity measures for the characterization of direct and indirect interactions within human regulatory pathways. The characterization would help the integration of prior pathway knowledge for the modeling. RESULTS: Our analysis indicates information content-based measures outperform graph structure-based measures for stratifying protein interactions. Measures in terms of GO biological process and molecular function annotations can be used alone or together for the validation of protein interactions involved in the pathways. However, GO cellular component-derived measures may not have the ability to separate true positives from noise. Furthermore, we demonstrate that the functional similarity of proteins within known regulatory pathways decays rapidly as the path length between two proteins increases. Several logistic regression models are built to estimate the confidence of both direct and indirect interactions within a pathway, which may be used to score putative pathways inferred from a scaffold of molecular interactions.

Databases, Protein↗
PubMed2001

Data, design, and background knowledge in etiologic inference.

I use two examples to demonstrate that an appropriate etiologic analysis of an epidemiologic study depends as much on study design and background subject-matter knowledge as on the data. The demonstration is facilitated by the use of causal graphs.

Case-Control Studies↗
PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗
PubMed2005

Finding local community structure in networks.

Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k2d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k). We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.

Journal Article↗
PubMed1995

A conceptual graphs modeling of UMLS components.

The Unified Medical Language System (UMLS) of the U.S. National Library of Medicine is a complex collection of terms, concepts, and relationships derived from standard classifications. Potential applications would benefit from a high level representation of its components. This paper proposes a conceptual representation of both the Metathesaurus and the Semantic Network of the UMLS based on conceptual graphs. It shows that the addition of a dictionary of concepts to the UMLS knowledge base allows the capability to exploit it pertinently. This dictionary defines more precisely the core concepts and adds constraints on their use. Constraints are dedicated to guide an "intelligent" browsing of the UMLS knowledge sources.

Dictionaries as Topic↗