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Results for “knowledge graph”

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At least 181 records · Page 10Linked to original sources

PubMed2006

Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle.

MOTIVATION: To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks. RESULTS: Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge. AVAILABILITY: The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (http://gin.univ-mrs.fr/GINsim). Higher resolution state transitions graphs are also found on this web site (Model Repository page).

Animals↗

PubMed2001

Representing and querying conceptual graphs with relational database management systems is possible.

This is an experimental study on the feasibility of maintaining medical concept dictionaries in production grade relational database management systems (RDBMS.) In the past, RDBMS did not support transitive relational structures and had therefore been unsuitable for managing knowledge bases. The revised SQL-99 standard, however, may change this. In this paper we show that modern RDBMS that support recursive queries are capable of querying transitive relationships in a generic data model. We show a simple but efficient indexed representation of transitive closure. We could confirm that even challenging combined transitive relationships can be queried in SQL.

Artificial Intelligence↗

PubMed1984

Automated computer evaluation of time-varying cryomicroscopical images.

A system has been developed to perform automatic computerized recognition, tracking, and quantitative morphological analysis of viable cells in freezing solutions. Cryomicroscopical image sequences of freezing cells are digitized and analyzed by computer. Image-processing techniques are used which are insensitive to contrast fluctuations from image to image, and which perform well even in noisy, cluttered images. The generalized Hough transform is used for shape detection, and a heuristic graph-search boundary completion algorithm is applied for shape extraction. The extracted cell shape may be analyzed for changes in cross-sectional area, perimeter length, shape deformation, and other metrics of interest. Knowledge from the shape-extraction phase is used to form a prediction of what shape the cell will be in the next image frame, and thus what to look for in the next shape-detection phase. This combination of knowledge-feedback with a powerful shape-detection technique produces an automatic, dynamic shape-recognition scheme capable of accurate recognition and analysis of the cells regardless of how deformed they may become during the freezing sequence. Example performance of the system is illustrated for a series of micrographs of freezing granulocytes.

Blood Preservation↗

PubMedDate not supplied

Spec2D: a structure elucidation system based on 1H NMR and H-H COSY spectra in organic chemistry.

A system for structure elucidation based on proton NMR spectra has been developed. The system, named Spec2D (system for spectra from 2D-NMR), incorporates 1H NMR and H-H correlation spectroscopy (COSY) spectral information obtained from 2D-NMR experiments. 2D-NMR is important for the structure elucidation because it provides information about the relationships among differently situated protons in the structures of unknown compounds. The system uses the concepts of molecular graphs. The improved representation of substructures as well as several novel algorithms for structure generation have been devised to solve the combinatorial problem and to reduce the processing time. Spec2D consists of a knowledge base, an analysis module, and a candidate structure generator module. Spec2D proposes candidate structures from only 1H NMR and H-H COSY spectral information of an unknown compound without any 13C NMR spectral or structural information, such as molecular formulas. Spec2D has the capability to propose the "new" structure of an unknown compound, if the corresponding substructures are included in the knowledge base.

Journal Article↗

PubMed2006

Medical image segmentation using minimal path deformable models with implicit shape priors.

This paper presents a new method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the short-comings of existing deformable models and has been successfully applied to segmenting various medical images.

Algorithms↗

PubMed1999

A method of estimating fetal dose during brain radiation therapy.

PURPOSE: To develop a simple method of estimating fetal dose during brain radiation therapy. METHODS AND MATERIALS: An anthropomorphic phantom was modified to simulate pregnancy at 12 and 24 weeks of gestation. Fetal dose measurements were carried out using thermoluminescent dosimeters. Brain radiation therapy was performed with two lateral and opposed fields using 6 MV photons. Three sheets of lead, 5.1-cm-thick, were positioned over the phantom's abdomen to reduce fetal exposure. Linear and nonlinear regression analysis was used to investigate the dependence of radiation dose to an unshielded and/or shielded fetus upon field size and distance from field isocenter. RESULTS: Formulas describing the exponential decrease of radiation dose to an unshielded and/or shielded fetus with distance from the field isocenter are presented. All fitted parameters of the above formulas can be easily derived using a set of graphs showing their correlation with field size. CONCLUSION: This study describes a method of estimating fetal dose during brain radiotherapy, accounting for the effects of gestational age, field size and distance from field isocenter. Accurate knowledge of absorbed dose to the fetus before treatment course allows for the selection of the proper irradiation technique in order to achieve the maximum patient benefit with the least risk to the fetus.

Adolescent↗

PubMed2025

PLNMFG: Pseudo-label guided non-negative matrix factorization model with graph constraint for single-cell multi-omics data clustering.

The development of single-cell multi-omics sequencing technologies has enabled the simultaneous analysis of multi-omics data within the same cell. Accurate clustering of these cells is crucial for downstream analyses of complex biological functions. Despite significant advances in multi-omics integration approaches, current methodologies exhibit two major limitations. First, they inadequately incorporate prior biological knowledge from various omic layers. Second, these methods often conduct independent dimensionality reduction on individual omic datasets, thereby failing to capture the intrinsic complementary information and potentially overlooking crucial cross-platform interactions. Motivated by these, this study investigates a non-negative matrix factorization model called PLNMFG, which integrates the unified latent representation learning that retains the features between and within omics and the cluster structure learning that retains the intrinsic structure of the data into one joint framework. Specially, PLNMFG performs adaptive imputation to handle dropout events and uses prior pseudo-labels as constraints during the process of collective non-negative matrix factorization, as a result, a more robust latent representation that preserves the double similarity information is obtained. Graph Laplacian constraint is applied during clustering which further preserves structure characteristic of multi-omics data. In addition, the weight of each omic is adaptively learned based on the omic contribution. A series of experiments on 8 benchmark datasets show that our model performs well in terms of clustering accuracy and computational efficiency.

Single-Cell Analysis↗

PubMedDate not supplied

Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data.

Mounting evidence shows that many protein complexes are conserved in evolution. Here we use conservation to find complexes that are common to the yeast S. cerevisiae and the bacteria H. pylori. Our analysis combines protein interaction data that are available for each of the two species and orthology information based on protein sequence comparison. We develop a detailed probabilistic model for protein complexes in a single species and a model for the conservation of complexes between two species. Using these models, one can recast the question of finding conserved complexes as a problem of searching for heavy subgraphs in an edge- and node-weighted graph, whose nodes are orthologous protein pairs. We tested this approach on the data currently available for yeast and bacteria and detected 11 significantly conserved complexes. Several of these complexes match very well with prior experimental knowledge on complexes in yeast only and serve for validation of our methodology. The complexes suggest new functions for a variety of uncharacterized proteins. By identifying a conserved complex whose yeast proteins function predominantly in the nuclear pore complex, we propose that the corresponding bacterial proteins function as a coherent cellular membrane transport system. We also compare our results to two alternative methods for detecting complexes and demonstrate that our methodology obtains a much higher specificity.

Bacterial Proteins↗

PubMed2000

Gray-scale skeletonization of small vessels in magnetic resonance angiography.

Interpretation of magnetic resonance angiography (MRA) is problematic due to complexities of vascular shape and to artifacts such as the partial volume effect. We present new methods to assist in the interpretation of MRA. These include methods for detection of vessel paths and for determination of branching patterns of vascular trees. They are based on the ordered region growing (ORG) algorithm that represents the image as an acyclic graph, which can be reduced to a skeleton by specifying vessel endpoints or by a pruning process. Ambiguities in the vessel branching due to vessel overlap are effectively resolved by heuristic methods that incorporate a priori knowledge of bifurcation spacing. Vessel paths are detected at interactive speeds on a 500-MHz processor using vessel endpoints. These methods apply best to smaller vessels where the image intensity peaks at the center of the lumen which, for the abdominal MRA, includes vessels whose diameter is less than 1 cm.

Algorithms↗

PubMed1996

Developmental dyslexia: a motor-articulatory feedback hypothesis.

Reading is mediated by parallel and widely distributed modular systems. There are, therefore, multiple loci in these systems where dysfunction may lead to developmental dyslexia. However, most normal children learn to read using the alphabetic system. Learning to use this system requires awareness that words are comprised of a system of speech sounds (phonological awareness) and the knowledge of how to convert letters (graphemes) into these speech sounds (phonemes). Most dyslexic children have deficient phonological awareness and have difficulty converting graphemes into phonemes. Studies of patients with acquired lesions who are unable to convert graph-emes into phonemes, as well as positron emission tomographic studies of normal subjects, suggest that the left inferior frontal lobe is important in phonologic reading. Phonetic gestures are represented in the brain as invariant motor commands that program the articulators. Phonologic reading may activate the left inferior frontal lobe because grapheme-to-phoneme conversion requires activation of these motor-articulatory gestures. Dyslexic children are unaware of the position of their articulators during speech. The inability to associate the position of their articulators with speech sounds may impair the development of phonological awareness and the ability to convert graphemes to phonemes. Unawareness of their articulators may be related to programming or feedback deficits.

Auditory Pathways↗

PubMed2006

Bayesian graphical models for genomewide association studies.

As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being collected in such studies. We use discrete graphical models as a data-mining tool, searching for single- or multilocus patterns of association around a causative site. The approach is fully Bayesian, allowing us to incorporate prior knowledge on the spatial dependencies around each marker due to linkage disequilibrium, which reduces considerably the number of possible graphical structures. A Markov chain-Monte Carlo scheme is developed that yields samples from the posterior distribution of graphs conditional on the data from which probabilistic statements about the strength of any genotype-phenotype association can be made. Using data simulated under scenarios that vary in marker density, genotype relative risk of a causative allele, and mode of inheritance, we show that the proposed approach has better localization properties and leads to lower false-positive rates than do single-locus analyses. Finally, we present an application of our method to a quasi-synthetic data set in which data from the CYP2D6 region are embedded within simulated data on 100K single-nucleotide polymorphisms. Analysis is quick (<5 min), and we are able to localize the causative site to a very short interval.

Bayes Theorem↗

PubMed2004

Universal electronic health record MUDR.

One of the important research tasks of the European Centre for Medical Informatics, Statistics and Epidemiology - Cardio (EuroMISE Centre - Cardio) is the applied research in the field of electronic health record design including electronic medical guidelines and intelligent systems for data mining and decision support. The research in the field of data storage and data acquisition was inspired by several European projects and standards, mostly by the I4C and TripleC projects. Based on experience gathered during cooperation in the TripleC project we have proposed a description of a flexible information storage model. The motivation for this effort was the large variability of the set of collected features in different departments - including temporal variability. Therefore, a dynamically extensible and modifiable structure of items is needed. In our model we use two basic structures called the knowledge base and data files. The main function of the knowledge base is to express the hierarchy of collectable features - medical concepts, their characteristics and relations among them. The data files structure is used to store the patient's data itself. These two structures can be described using graph theory expressions. Based on this model, a three-layer system architecture named "Multimedia Distributed Record" (MUDR) has been proposed and implemented. During the implementation, modern technologies such as Web Services, SOAP and XML were used. For the practical usage of EHR MUDR, an intelligent application called MUDRc (MUDR Client) was created. It enables physicians to use EHR MUDR in a flexible way. During the development process, maximum emphasis was placed on user-friendliness and comfortable usage of this application. Several methods of data entry can be used: pre-defined forms, direct entry into the tree data structure of the EHR MUDR, or automatic unstructured free-text report parsing and data retrieval. The system enables fast and simple importing and exporting of data as well. The system integrates modern multimedia formats (X-ray photos, sonography and other pictures, video-sequences, audio records) as well as progressive methods of decision support systems realized by medical guidelines and other modules.

Artificial Intelligence↗

PubMedDate not supplied

Substructure mining using elaborate chemical representation.

Substructure mining algorithms are important drug discovery tools since they can find substructures that affect physicochemical and biological properties. Current methods, however, only consider a part of all chemical information that is present within a data set of compounds. Therefore, the overall aim of our study was to enable more exhaustive data mining by designing methods that detect all substructures of any size, shape, and level of chemical detail. A means of chemical representation was developed that uses atomic hierarchies, thus enabling substructure mining to consider general and/or highly specific features. As a proof-of-concept, the efficient, multipurpose graph mining system Gaston learned substructures of any size and shape from a mutagenicity data set that was represented in this manner. From these substructures, we extracted a set of only six nonredundant, discriminative substructures that represent relevant biochemical knowledge. Our results demonstrate the individual and synergistic importance of elaborate chemical representation and mining for nonlinear substructures. We conclude that the combination of elaborate chemical representation and Gaston provides an excellent method for 2D substructure mining as this recipe systematically explores all substructures in different levels of chemical detail.

Databases as Topic↗

PubMed2006

Dense subgraph computation via stochastic search: application to detect transcriptional modules.

MOTIVATION: In a tri-partite biological network of transcription factors, their putative target genes, and the tissues in which the target genes are differentially expressed, a tightly inter-connected (dense) subgraph may reveal knowledge about tissue specific transcription regulation mediated by a specific set of transcription factors-a tissue-specific transcriptional module. This is just one context in which an efficient computation of dense subgraphs in a multi-partite graph is needed. RESULT: Here we report a generic stochastic search based method to compute dense subgraphs in a graph with an arbitrary number of partitions and an arbitrary connectivity among the partitions. We then use the tool to explore tissue-specific transcriptional regulation in the human genome. We validate our findings in Skeletal muscle based on literature. We could accurately deduce biological processes for transcription factors via the tri-partite clusters of transcription factors, genes, and the functional annotation of genes. Additionally, we propose a few previously unknown TF-pathway associations and tissue-specific roles for certain pathways. Finally, our combined analysis of Cardiac, Skeletal, and Smooth muscle data recapitulates the evolutionary relationship among the three tissues.

Algorithms↗

PubMed2005

Planners and their cognitive maps: an analysis of domain representations using multi dimensional scaling.

We expected planners working on the planning problems at a particular department for many years to have similar ideas about their work domain. However, we found the opposite when we analyzed the results of an extensive knowledge elicitation study we performed on 25 planners working in the rolling stock and rolling staff planning department of the Netherlands Railways (in Dutch: NS). The planners were asked to model their domain by means of card sorting and graph-positioning methods using domain-related objects and relations. When we applied multi dimensional scaling to the outcome of this study, we found large differences in the individual cognitive maps thus created. In this article, we describe the analyses and discuss our findings about planners' cognitive maps. These maps may have implications for the organization of planning in general, for training of planners, and for software design.

Administrative Personnel↗

PubMed2000

Verbal and visual causal arguments.

The present paper analyzes how verbalizations and visualizations can be used to justify and dispute causal claims. The analysis is based on a taxonomy of 27 causal arguments as they appear in ordinary language. It is shown how arguments from spatio-temporal contiguity, covariation, counterfactual necessity, and causal mechanisms, to name only a few, are visualized in persuasive uses of tables, graphs, time series, causal diagrams, drawings, maps, animations, photos, movies, and simulations. The discussion centers on how these visual media limit the argumentative moves of justifying, disputing, and qualifying claims; how they constrain the representation of observational, explanatory, and abstract knowledge in the premises of causal arguments; and how they support and externalize argument-specific inferences, namely generalizations, comparisons, mental simulations, and causal explanations.

Humans↗

PubMedDate not supplied

Automatic derivation of initial match points for paired digital images of skin.

Computerized matching of skin images has been suggested as a means of screening for changes indicative of malignant melanoma. Matching or "registering" each lesion in a pair of images plays an important role in this process. Point-pattern matching algorithms based upon correlation or geometric transformations of the image pairs have been shown to be effective for this registration but require knowledge of one or more "initial match" points which are known to be the same on both images. A method is described for automatically finding these initial match points with a high degree of success. Our algorithm uses the so-called "Gabriel graph" representation of the paired images to select sets of probable matching points. Performance of the algorithm has been measured in realistic trials, using images of patients with large numbers of pigmented lesions. Results show that the single best match chosen is correct greater than 99% of the time, while over 93% of the first three consecutive matches will be chosen correctly. It is likely that these results can be improved through the use of additional nonpositional information or additional image processing.

Algorithms↗

PubMed2005

Interactions among the imagination, expertise reversal, and element interactivity effects.

Interactions among the imagination, expertise reversal, and element interactivity effects were investigated in 2 experiments. In Experiment 1, less knowledgeable primary school students learning to use a bus timetable produced better performance under study than imagination conditions, but an increase in their experience reversed the result, producing the imagination effect. Experiment 2, in which students were taught to use a temperature line graph, replicated these results and demonstrated that the effects were more likely to be obtained using complex material. It was concluded that learners can engage more successfully in imagination procedures when learning has proceeded sufficiently to permit the information to be processed in working memory and that working memory limitations are of little consequence except when dealing with complex information.

Cognition↗