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PubMed1989

Knowledge management as a decision support method: a diagnostic workup strategy application.

We have explored the potential of a computer-based approach called "knowledge management" to aid in clinical problem solving and education. The major features of the approach are its ability to support flexible and immediate access by a user to relevant knowledge and annotation and organization of the knowledge for personal use and subsequent retrieval. We illustrate this approach with its application to diagnostic workup strategy problems. In this application, knowledge may be in the form of static narrative text, diagrams, pictures, graphs, tables, flow charts, or bibliographic citations. Other more dynamic forms of knowledge may be the result of simulations, "what if" analyses or modeling, quantitative mathematical or statistical calculation, or heuristic inference. User assessment has demonstrated the system's ease of use and user perception of its desirability, but underscores the need for a "critical mass" of knowledge before such an approach will be widely utilized.

Decision Support Techniques↗
PubMed1998

Navigation and acquisition of spatial knowledge in a virtual maze.

Spatial behavior in humans and animals includes a wide variety of behavioral competences and makes use of a large number of sensory cues. Here we studied the ability of human subjects to search locations, to find shortcuts and novel paths, to estimate distances between remembered places, and to draw sketch maps of the explored environment; these competences are related to goal-independent memory of space, or cognitive maps. Information on spatial relations was restricted to two types: a visual motion sequence generated by simulated movements in a virtual maze and the subject's own movement decisions defining the path through the maze. Visual information was local (i.e., no global landmarks or compass information was provided). Other position and movement information (vestibular or proprioceptive) was excluded. The amount of visual information provided was varied over four experimental conditions. The results indicate that human subjects are able to learn a virtual maze from sequences of local views and movements. The information acquired is local, consisting of recognized positions and movement decisions associated to them. Although simple associations of this type can be shown to be present in some subjects, more complete configurational knowledge is acquired as well. The results are discussed in a view-based framework of navigation and the representation of spatial knowledge by means of a view graph.

Adolescent↗
PubMed1990

A microcomputer-photocell system to monitor periparturient activity of sows and transfer data to remote location.

A microcomputer-photocell system to record, summarize and transfer data on the postural changes of crate-confined sows was developed. The system uses an infrared photocell system mounted on farrowing crates to detect changes in sow body postures prior to parturition. When a sow breaks the infrared beam by standing or sitting, the lack of reflection back to the photocell activates a relay. The relays are connected to an input/output card inside the primary ("host") microcomputer. A second microcomputer, at a location remote to the animal facility, and the host computer both are equipped with multi-tasking operating systems. The host computer uses a data acquisition program written in BASIC to record posture changes, the sow's identifying number, date and time. Both computers are equipped with communications software so that the host computer can transfer data to the remove location while continuing to collect data. Either computer can be used to summarize, graph and examine the accumulated data. Knowledge of periparturient activity patterns allows the observer to predict farrowing time and to determine whether farrowing has begun without being present in the farrowing facility.

Animals↗
PubMed1988

[Expert systems in medical emergencies].

Computers have already been introduced into the field of medical applications and many support systems have been developed. Recently expert or knowledge-based systems have been employed in some specific fields. A project for the creation of an expert system for use in toxicological emergencies is presented. This system is designed to give the physician continuous support and to employ computer formulae as similar as possible to medical reasoning. For this reason, medical knowledge was examined and the event graphs, frames and production rules to represent it were chosen.

Amanita↗
PubMedDate not supplied

A knowledge-based weighting approach to ligand-based virtual screening.

On the basis of the recently introduced reduced graph concept of ErG (extending reduced graphs), a straightforward weighting approach to include additional (e.g., structural or SAR) knowledge into similarity searching procedures for virtual screening (wErG) is proposed. This simple procedure is exemplified with three data sets, for which interaction patterns available from X-ray structures of native or peptidomimetic ligands with their target protein are used to significantly improve retrieval rates of known actives from the MDL Drug Report database. The results are compared to those of other virtual screening techniques such as Daylight fingerprints, FTrees, UNITY, and various FlexX docking protocols. Here, it is shown that wErG exhibits a very good and stable performance independent of the target structure. On the basis of this (and the fact that ErG retrieves structurally more dissimilar compounds due to its potential to perform scaffold-hopping), the combination of wErG and FlexX is successfully explored. Overall, wErG is not only an easily applicable weighting procedure that efficiently identifies actives in large data sets but it is also straightforward to understand for both medicinal and computational chemists and can, therefore, be driven by several aspects of project-related knowledge (e.g., X-ray, NMR, SAR, and site-directed mutagenesis) in a very early stage of the hit identification process.

Computational Biology↗
PubMed1995

Current trends with natural language processing.

Natural Language Processing in the medical domain becomes more and more powerful, efficient, and ready to be used in daily practice. The needs for such tools are enormous in the medical field, due to the vast amount of written texts for medical records. In the authors' point of view, the Electronic Patient Record (EPR) is achieved neither with Information Systems of all kinds nor with commercially available word processing systems. Natural Language Processing (NLP) is one dimension of the EPR, as well as Image Processing and Decision Support Systems. Analysis of medical texts to facilitate indexing and retrieval is well known. The need for a generation tool is to produce progress notes from menu driven systems. The computer systems of tomorrow cannot miss any single dimension. Since 1988, we've been developing an NLP system; it is supported by the European program AIM (Advanced Informatics in Medicine) within the GALEN and HELIOS consortium and the CERS (Commission d'Encouragement á la Recherche Scientifique) in Switzerland. The main directions of development are: a medical language analyzer, a language generator, a query processor, and dictionary building tools to support the Medical Linguistic Knowledge Base (MLKB). The knowledge representation schema is essentially based on Sowa's conceptual graphs, and the MLKB is multilingual from its design phase; it currently incorporates the English and the French languages; it will also continue using German. The goal of this demonstration is to provide evidence of what exists today, what will be soon available, and what is planned for the long term. Complete sentences will be processed in real time, and the browsing capabilities of the MLKB will be exercised. In particular, the following features will be presented: Analysis of complete sentences with verbs and relatives, as extracted from clinical narratives, with special attention to the method of "proximity processing" as developed in our group and the rule based approach to language description to resolve the specific surface language problems as well as the language independent semantic situations. Comparison of results for English, French, and German sentences, showing the commonalities between these languages and, therefore, the re-usable features and the language specific aspects. Generation of noun phrases in English and French, showing the opportunities for translation between these two languages. Application of the analyzer to build a knowledge representation of ICD under the form of conceptual graphs and presentation of the possibilities of a natural language encoding of diagnosis. Strategies for query processing through a sample of abdominal ultrasonography reports, which have been analyzed and stored under the form of conceptual graphs. Feeding in and browsing of the Medical Linguistic Knowledge Base and other Dictionary Building Tools, using the perspective of an international initiative to converge towards a multilingual universal solution, valid for the medical domain. The demonstration platform is Microsoft Windows 4 on a PC, with Microsoft Visual Basic as the GUI and Quintus Prolog as NLP tools language. The same programs were originally developed for Unix-based workstations and are available on multiple platforms under Motif and X11. .

European Union↗
PubMed2000

Disease management research using event graphs.

Event Graphs, conditional representations of stochastic relationships between discrete events, simulate disease dynamics. In this paper, we demonstrate how Event Graphs, at an appropriate abstraction level, also extend and organize scientific knowledge about diseases. They can identify promising treatment strategies and directions for further research and provide enough detail for testing combinations of new medicines and interventions. Event Graphs can be enriched to incorporate and validate data and test new theories to reflect an expanding dynamic scientific knowledge base and establish performance criteria for the economic viability of new treatments. To illustrate, an Event Graph is developed for mastitis, a costly dairy cattle disease, for which extensive scientific literature exists. With only a modest amount of imagination, the methodology presented here can be seen to apply modeling to any disease, human, plant, or animal. The Event Graph simulation presented here is currently being used in research and in a new veterinary epidemiology course.

Animals↗
PubMed2006

Combining functional and topological properties to identify core modules in protein interaction networks.

Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.

Algorithms↗
PubMed2004

A knowledge-based clustering algorithm driven by Gene Ontology.

We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.

Algorithms↗
PubMed1997

Vagaries in the delimitation of character states in quantitative variation--an experimental study.

An experimental study on the delimitation of character states in continuous variation indicates that (1) the way data are presented influences the assignment of character states and (2) states in the same data set are delimited in various ways by different individuals. Forty-nine individuals were given a set of graphs denoting variation of 10 characters in the genus Kalmia (Ericaceae) and outgroups, all identification having been removed from the graphs. The variation was represented in one of three ways: as 95% confidence intervals on a linear scale, as 95% confidence intervals on a log10 scale, or with bars showing SD x 2 on a linear scale. No two individuals scored a set of graphs in the same way, and only one character in one representation was scored identically by all individuals; the scoring for this character was completely different when the ordinate was changed from linear to logarithmic. Together, the 49 individuals delimited states within each character between 9 and 16 different ways. In general, variation represented by 2 x SD bars elicited the largest numbers of different scorings, yet with a relatively low number of states; the complexity of the patterns in the graphs in this representation was greatest. Expert knowledge appears to be of dubious value in delimiting states in such variation, and if such characters are to be used in phylogenetic analyses, states could be delimited by people who know nothing of the details of the study being scored; in any case, presentation of data and an explicit protocol to follow when delimiting states are essential. In converting data of this type into character states, psychological factors are particularly likely to come into play. Other implications of our experiments include the severe underdetermination of some phylogenetic hypotheses by observation and the heterogeneous nature of morphological data.

Data Interpretation, Statistical↗
PubMed2005

tRNA structure from a graph and quantum theoretical perspective.

One of the objectives of theoretical biochemistry is to find a suitable representation of molecules allowing us to encode what we know about their structures, interactions and reactivity. Particularly, tRNA structure is involved in some processes like aminoacylation and genetic code translation, and for this reason these molecules represent a biochemical object of the utmost importance requiring characterization. We propose here two fundamental aspects for characterizing and modeling them. The first takes into consideration the connectivity patterns, i.e. the set of linkages between atoms or molecular fragments (a key tool for this purpose is the use of graph theory), and the second one requires the knowledge of some properties related to the interactions taking place within the molecule, at least in an approximate way, and perhaps of its reactivity in certain means. We used quantum mechanics to achieve this goal; specifically, we have used partial charges as a manifestation of the reply to structural changes. These charges were appropriately modified to be used as weighted factors for elements constituting the molecular graph. This new graph-tRNA context allow us to detect some structure-function relationships.

Base Sequence↗
PubMed2004

Semantic graphs and associative memories.

Graphs have been increasingly utilized in the characterization of complex networks from diverse origins, including different kinds of semantic networks. Human memories are associative and are known to support complex semantic nets; these nets are represented by graphs. However, it is not known how the brain can sustain these semantic graphs. The vision of cognitive brain activities, shown by modern functional imaging techniques, assigns renewed value to classical distributed associative memory models. Here we show that these neural network models, also known as correlation matrix memories, naturally support a graph representation of the stored semantic structure. We demonstrate that the adjacency matrix of this graph of associations is just the memory coded with the standard basis of the concept vector space, and that the spectrum of the graph is a code invariant of the memory. As long as the assumptions of the model remain valid this result provides a practical method to predict and modify the evolution of the cognitive dynamics. Also, it could provide us with a way to comprehend how individual brains that map the external reality, almost surely with different particular vector representations, are nevertheless able to communicate and share a common knowledge of the world. We finish presenting adaptive association graphs, an extension of the model that makes use of the tensor product, which provides a solution to the known problem of branching in semantic nets.

Journal Article↗
PubMed1989

Inference from clines stabilized by frequency-dependent selection.

Frequency-dependent selection against rare forms can maintain clines. For weak selection, s, in simple linear models of frequency-dependence, single locus clines are stabilized with a maximum slope of between square root of s/square root of 8 sigma and square root of s/square root of 12 delta, where sigma is the dispersal distance. These clines are similar to those maintained by heterozygote disadvantage. Using computer simulations, the weak-selection analytical results are extended to higher selection pressures with up to three unlinked genes. Graphs are used to display the effect of selection, migration, dominance, and number of loci on cline widths, speeds of cline movements, two-way gametic correlations ("linkage disequilibria"), and heterozygote deficits. The effects of changing the order of reproduction, migration, and selection, are also briefly explored. Epistasis can also maintain tension zones. We show that epistatic selection is similar in its effects to frequency-dependent selection, except that the disequilibria produced in the zone will be higher for a given level of selection. If selection consists of a mixture of frequency-dependence and epistasis, as is likely in nature, the error made in estimating selection is usually less than twofold. From the graphs, selection and migration can be estimated using knowledge of the dominance and number of genes, of gene frequencies and of gametic correlations from a hybrid zone.

Computer Simulation↗
PubMed2003

An automated method for finding molecular complexes in large protein interaction networks.

BACKGROUND: Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. RESULTS: This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. CONCLUSION: Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE.

Algorithms↗
PubMed1994

Users conceptual views on medical information databases.

As information databases we consider all the kinds of information repositories that are handled by computer systems. When querying very large information databases, the end-users are often faced with the problem to parse their questions efficiently into the query languages of the computer systems. Conceptual graphs were initially designed for natural language analysis and understanding. Due to their closeness to semantic networks, their expressiveness is powerful enough to be applied to knowledge representation and use by computer systems. This work demonstrates that conceptual graphs are a suitable means to model both the information in patient databases and the queries to these databases, and that operations on graphs can compute the pattern matching process needed to provide the answers. A prototype that exploits this model is presented. Experiments have been made with the material furnished by the Unified Medical Language System project (version 2, 1992) of the National Library of Medicine, USA.

Abstracting and Indexing↗
PubMed1993

A conceptual model for information retrieval with UMLS.

Information retrieval in large information databases is a non-deterministic process which needs a sequence of search steps generally. One of the main problems to which the end-users are faced is to parse efficiently their questions into the query language that the computer systems allow. Conceptual graphs were initially designed for natural language analysis and understanding. Due to their closeness to semantic networks, their expressiveness is powerful enough to be applied to knowledge representation and use by computer systems. This work demonstrates that conceptual graphs are a suitable means to model the end-users querieson the basis of the thesaurus and the semantic network of the UMLS project.

Information Storage and Retrieval↗
PubMed2000

A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters.

The availability of computerized knowledge on biochemical pathways in the KEGG database opens new opportunities for developing computational methods to characterize and understand higher level functions of complete genomes. Our approach is based on the concept of graphs; for example, the genome is a graph with genes as nodes and the pathway is another graph with gene products as nodes. We have developed a simple method for graph comparison to identify local similarities, termed correlated clusters, between two graphs, which allows gaps and mismatches of nodes and edges and is especially suitable for detecting biological features. The method was applied to a comparison of the complete genomes of 10 microorganisms and the KEGG metabolic pathways, which revealed, not surprisingly, a tendency for formation of correlated clusters called FRECs (functionally related enzyme clusters). However, this tendency varied considerably depending on the organism. The relative number of enzymes in FRECs was close to 50% for Bacillus subtilis and Escherichia coli, but was <10% for SYNECHOCYSTIS: and Saccharomyces cerevisiae. The FRECs collection is reorganized into a collection of ortholog group tables in KEGG, which represents conserved pathway motifs with the information about gene clusters in all the completely sequenced genomes.

Algorithms↗
PubMed2005

Decision support system for the diagnosis of schizophrenia disorders.

Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.

Decision Support Systems, Clinical↗