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PubMed1995

Description and advantages of an index-driven medical knowledge base.

In the FRAMEMED system design, the inherent attributes of its concepts are expressed in the hierarchical lists of its 26 Elements (e.g., Agents, Clinical Manifestations, Diseases, Tests, etc.). These concepts, contained in regular structures, are then alphabetized by phrase (and synonym), forming a combined index in which the user may quickly find a concept either alphabetically or hierarchically. Stored in the structures of the index are pointers to four types of knowledge records: 1) Descriptive (definition); 2) Relational (incidental attributes); 3) Conditional (rules); and 4) Procedural (how to). In contrast to the index which is stored in regular structures for rapid access (like relational databases), the knowledge records are stored in free text (variable length) and may include pointers to imaging and audio records. A particular feature of the FRAMEMED system is careful attention to modifiers, an aspect usually not emphasized in other systems. In trying to structure the free text describing a patient encounter, for example, the major concepts such as cough, fever, stiff neck, etc., are relatively easy to code (although a common system has not yet been agreed upon). The devil lies in the modifiers such as 'history of', 'severe,' 'constant,' 'absent,' 'left,' 'abnormal,' etc., particularly when there is concatenation of modifiers modifying modifiers. Our Relational records (in our knowledge base) and our Chronological Medical Records (CMR) in our patient record have the same format, namely, a title, several related items, and a date/author. For example, our disease profile (Relational record) for 'Influenza' might include 'cough,' 'fever,' and 'stiff neck.' The CMR of a particular patient encounter might include the same items. The only differences would be the title (disease name for the disease profile, date for the CMR, and the omission of the redundant date in the date/author line of the CMR). Each related item in either of these records is expressed in a four-part string, namely: 1) Relation; 2) Code; 3) Phrase; and 4) Comment. Modifiers (common ASCII symbols) are structured into each of these parts. For example, if the patient did not have 'cough,' the default '+' in the Relation would be edited to a '-', while 'history of' cough would be '>'. Each Relation can be graded (on a 5-level scale) for both importance and frequency. The Code for a test can carry the result suffix, '+ positive/high,' '-negative/low,' '# abnormal (qualitatively)', or '1 unremarkable/normal.' Topological information, such as '/left,' can be appended to a Code. If the cough is getting worse, its code can have the suffix, '<'. The standardized Phrases associated with the Codes come from the hierarchical lists of the index section described earlier. Phrases are not stored, being rematched to the codes as needed for user display. This practice not only saves memory space but allows a CMR encounter recorded in one language to be displayed in another second language subsequently, requiring only the existence of the hierarchical code/phrase in the second language. A free-text Comment is allowed for any related item in a Relational record or CMR, to allow the doctor to add important nuances such as 'worse on arising' or for a numeric result such as a test result or a thermometer reading. Some structuring can be accommodated in the Comment by introducing symbols such as '> relieved by,' followed by a list containing entries such as 'antacids.' Time can be sturctured through symbol lists such as '@-2 mo' representing '2 months previously.' Because Relational records in the knowledge ase and patient encoutner records in the CMR both display findings in hierarchical order; all similar items (e.g., Agents, Clinical Manifestations, Tests, Procedures, etc.) occur together and in an unique order. (abstract truncated)

Abstracting and Indexing↗

PubMed2002

Inferring genotype from clinical phenotype through a knowledge based algorithm.

Genomic information is becoming increasingly useful for studying the origins of disease. Recent studies have focused on discovering new genetic loci and the influence of these loci upon disease. However, it is equally desirable to go in the opposite direction--that is, to infer genotype from the clinical phenotype for increased efficiency of treatment. This paper proposes a methodology for such inference. Our method constructs a simple knowledge-based model without the need of a domain expert and is useful in situations that have very little data and/or no training data. The model relates a disease's symptoms to particular clinical states of the disease. Clinical information is processed using the model, where appropriate weighting of the symptoms is learned from observed diagnoses to subsequently identify the state of the disease presented in hospital visits. This approach applies to any simple genetic disorder that has defined clinical phenotypes. We demonstrate the use of our methods by inferring age of onset and DNA mutations for Huntington's disease patients.

Algorithms↗

PubMed1992

Development of a knowledge base for diagnostic reasoning in cardiology.

This paper reports on a formative evaluation of the diagnostic capabilities of the Heart Failure Program, which uses a probability network and a heuristic hypothesis generator. Using 242 cardiac cases collected from discharge summaries at a tertiary care hospital, we compared the diagnoses of the program to diagnoses collected from cardiologists using the same information as was available to the program. With some adjustments to the knowledge base, the Heart Failure Program produces appropriate diagnoses about 90% of the time on this training set. The main reasons for the inappropriate diagnoses of the remaining 10% include inadequate reasoning with temporal relations between cause and effect, severity relations, and independence of acute and chronic diseases.

Artificial Intelligence↗

PubMed1993

European research efforts in medical knowledge-based systems.

This article describes the major projects going on in Europe in the field of Artificial Intelligence in Medicine. The important role of the Commission of the European Communities in providing the needed resources is stressed throughout the paper. Particular attention is given to the methodological and technological issues addressed by the European research teams, since the results which these teams accomplish are fundamental for a more extensive diffusion of knowledge-based systems in real medical settings. The variety of medical problems tackled shows that there is no field of medicine where the potential of advanced informatics technologies has not yet been assessed.

Artificial Intelligence↗

PubMed1991

An assessment of rural hospital trustees' health care knowledge base.

Rural hospital trustees are usually volunteers who serve important roles in the governance of a hospital and, therefore, in defining health care policy in their communities. Because most trustees are not health professionals, their orientation to the hospital and continuing education about the hospital present a special challenge to administrators. One hundred and three trustees from 10 rural hospitals in western New York were surveyed to better understand their demographics, their knowledge base regarding the hospital, and their roles as trustees. Sixty-six percent of the respondents were male and the average age of the sample was 48 years. Trustees had served an average of six years and spent seven hours per month on hospital business. Eighty-three percent recalled receiving some orientation. Answers about average hospital census, length of stay, payor type, and hospital services were correct less than 50 percent of the time. Trustees were aware that recent quality assurance guidelines increased their liability and half believed it was their most important activity. We conclude that greater effort should be applied to the orientation and continuing education of hospital trustees. Given the significant time commitment already asked of trustees, this education should be woven into the hospital governance routine.

Demography↗

PubMed1997

Knowledge-based model of a glucosyltransferase from the oral bacterial group of mutans streptococci.

Mutans streptococci glucosyltransferases catalyze glucosyl transfer from sucrose to a glucan chain. We previously identified an aspartyl residue that participates in stabilizing the glucosyl transition state. The sequence surrounding the aspartate was found to have substantial sequence similarity with members of alpha-amylase family. Because little is known of the protein structure beyond the amino acid sequence, we used a knowledge-based interactive algorithm, MACAW, which provided significant level of homology with alpha-amylases and glucosyltransferase from Streptococcus downei gtfI (GTF). The significance of GTF similarity is underlined by GTF/alpha-amylase residues conserved in all but one alpha-amylase invariant residues. Site-directed mutagenesis of the three GTF catalytic residues are homologous with the alpha-amylase catalytic triad. The glucosyltransferases are members of the 4/7-superfamily that have a (beta/alpha)8-barrel structure and belong to family 13 of the glycohydralases.

Algorithms↗

PubMed2002

Knowledge-based potential functions in protein design.

Predicting protein sequences that fold into specific native three-dimensional structures is a problem of great potential complexity. Although the complete solution is ultimately rooted in understanding the physical chemistry underlying the complex interactions between amino acid residues that determine protein stability, recent work shows that empirical information about these first principles is embedded in the statistics of protein sequence and structure databases. This review focuses on the use of 'knowledge-based' potentials derived from these databases in designing proteins. In addition, the data suggest how the study of these empirical potentials might impact our fundamental understanding of the energetic principles of protein structure.

Amino Acid Sequence↗

PubMed2004

Peptide backbone reconstruction using dead-end elimination and a knowledge-based forcefield.

A novel, yet simple and automated, protocol for reconstruction of complete peptide backbones from C(alpha) coordinates only is described, validated, and benchmarked. The described method collates a set of possible backbone conformations for each set of residue triads from a structural library derived from the PDB. The optimal permutation of these three residue segments of backbone conformations is determined using the dead-end elimination (DEE) algorithm. Putative conformations are evaluated using a pairwise-additive knowledge-based forcefield term and a fragment overlap term. The protocol described in this report is able to restore the full backbone coordinates to within 0.2-0.6 A of the actual crystal structure from C(alpha) coordinates only. In addition, it is insensitive to errors in the input C(alpha) coordinates with RMSDs of 3.0 A, and this is illustrated through application to deliberately distorted C(alpha) traces. The entire process, as described, is rapid, requiring of the order of a few minutes for a typical protein on a typical desktop PC. Approximations enable this to be reduced to a few seconds, although this is at the expense of prediction accuracy. This compares very favorably to previously published methods, being sufficiently fast for general use and being one of the most accurate methods. Because the method is not restricted to the reconstruction from only C(alpha) coordinates, reconstruction based on C(beta) coordinates is also demonstrated.

Hydrogen Bonding↗

PubMed2001

A knowledge-based system to aid with the clinical interpretation of complex serum protein data.

In every area of science workers are finding increasing difficulty in managing the volume of available data. In medicine, the accelerating pace has the worrisome overtones of our failing to provide up-to-date care for our patients. In other information-intensive areas, we rely heavily on software that manages much of the complexity unseen. Patient care could benefit enormously from the incorporation of "knowledge-based" programs to aid with diagnosis and management of many disorders. This article describes such a system designed to organize complex data which can be viewed as a test cluster aimed at many disorders pertinent to serum proteins. This program performs complex tasks such as reference range adjustment, ICD-9 code assignment, and searching for diagnostic "signatures", to generate clinically relevant text and simple graphics. The results have been remarkably accurate and produce repeatable results at the rate of approximately 10 cases per minute. The reluctance to embrace software assistance in laboratory medicine may have serious consequences in the short term and disastrous results within a decade. Expanding the limited algorithm described here to include more traditional chemistry testing could provide the very assistance that all in clinical care desire, a laboratory tool as powerful and adaptable as the traditional physical exam.

Artificial Intelligence↗

PubMed2005

Native and modeled disulfide bonds in proteins: knowledge-based approaches toward structure prediction of disulfide-rich polypeptides.

Structure prediction and three-dimensional modeling of disulfide-rich systems are challenging due to the limited number of such folds in the structural databank. We exploit the stereochemical compatibility of substructures in known protein structures to accommodate disulfide bonds in predicting the structures of disulfide-rich polypeptides directly from disulfide connectivity pattern and amino acid sequence in the absence of structural homologs and any other structural information. This knowledge-based approach is illustrated using structure prediction of 40 nonredundant bioactive disulfide-rich polypeptides such as toxins, growth factors, and endothelins available in the structural databank. The polypeptide conformation could be predicted in 35 out of 40 nonredundant entries (87%). Nonhomologous templates could be identified and models could be obtained within 2 A deviation from the query in 29 peptides (72%). This procedure can be accessed from the World Wide Web (http://www.ncbs.res.in/ approximately faculty/mini/dsdbase/dsdbase.html).

Algorithms↗

PubMed1999

Knowledge-based modeling of the serine protease triad into non-proteases.

The Asp-His-Ser triad of serine proteases has been regarded, in the present study, as an independent catalytic motif, because in nature it has been incorporated at the active sites of enzymes as diverse as the serine proteases and the lipases. Incorporating this motif into non-protease scaffolds, by rational design and mutagenesis, might lead to the generation of novel catalysts. As an aid to such experiments, a knowledge-based computer modeling procedure has been developed to model the protease Asp-His-Ser triad into non-proteases. Catalytic triads from a set of trypsin family proteases have been analyzed and criteria that characterize the geometry of the triads have been obtained. Using these criteria, the modeling procedure first identifies sites in non-proteases that are suitable for modeling the protease triad. H-bonded Asp-His-Ser triads, that mimic the protease catalytic triad in geometry, are then modeled in at these sites, provided it is stereochemically possible to do so. Thus non-protease sites at which H-bonded Asp-His-Ser triads are successfully modeled in may be considered for mutagenesis experiments that aim at introducing the protease triad into non-proteases. The triad modeling procedure has been used to identify sites for introducing the protease triad in three binding proteins and an immunoglobulin. A scoring function, depending on inter-residue distances, solvent accessibility and the substitution potential of amino acid residues at the modeling sites in the host proteins, has been used to assess the quality of the model triads.

Animals↗

PubMed1997

DROSOPOSON: a knowledge base on chromosomal localization of transposable element insertions in Drosophila.

MOTIVATION: What forces maintain transposable elements (TEs) in genomes and populations is one of the main questions to understand the dynamics of these elements, but the exact nature of these forces is still a matter of speculation. To test theoretical models of TE population dynamics, we need many data on the genomic distributions of various elements. These data are now accumulating for the species Drosophila melanogaster, but they are scattered in the literature. RESULTS: The knowledge base DROSOPOSON thus brings together: (1) data available on Drosophila chromosomal localizations of TE insertions and on features of the polytene chromosomes (DNA content, recombination rate, break-points, etc); (2) statistical methods aimed at analysing the distribution of the TE insertions along the chromosomes. In this paper, we present the structure of the base, the data and the statistical methods. Theoretical models of containment of TE copy number in Drosophila can thus be tested.

Animals↗

PubMed2005

Crystallographic refinement by knowledge-based exploration of complex energy landscapes.

Although X-ray crystallography remains the most versatile method to determine the three-dimensional atomic structure of proteins and much progress has been made in model building and refinement techniques, it remains a challenge to elucidate accurately the structure of proteins in medium-resolution crystals. This is largely due to the difficulty of exploring an immense conformational space to identify the set of conformers that collectively best fits the experimental diffraction pattern. We show here that combining knowledge-based conformational sampling in RAPPER with molecular dynamics/simulated annealing (MD/SA) vastly improves the quality and power of refinement compared to MD/SA alone. The utility of this approach is highlighted by the automated determination of a lysozyme mutant from a molecular replacement solution that is in congruence with a model prepared independently by crystallographers. Finally, we discuss the implications of this work on structure determination in particular and conformational sampling and energy minimization in general.

Amino Acid Sequence↗

PubMed2004

NRPS-PKS: a knowledge-based resource for analysis of NRPS/PKS megasynthases.

NRPS-PKS is web-based software for analysing large multi-enzymatic, multi-domain megasynthases that are involved in the biosynthesis of pharmaceutically important natural products such as cyclosporin, rifamycin and erythromycin. NRPS-PKS has been developed based on a comprehensive analysis of the sequence and structural features of several experimentally characterized biosynthetic gene clusters. The results of these analyses have been organized as four integrated searchable databases for elucidating domain organization and substrate specificity of nonribosomal peptide synthetases and three types of polyketide synthases. These databases work as the backend of NRPS-PKS and provide the knowledge base for predicting domain organization and substrate specificity of uncharacterized NRPS/PKS clusters. Benchmarking on a large set of biosynthetic gene clusters has demonstrated that, apart from correct identification of NRPS and PKS domains, NRPS-PKS can also predict specificities of adenylation and acyltransferase domains with reasonably high accuracy. These features of NRPS-PKS make it a valuable resource for identification of natural products biosynthesized by NRPS/PKS gene clusters found in newly sequenced genomes. The training and test sets of gene clusters included in NRPS-PKS correlate information on 307 open reading frames, 2223 functional protein domains, 68 starter/extender precursors and their specific recognition motifs, and also the chemical structure of 101 natural products from four different families. NRPS-PKS is a unique resource which provides a user-friendly interface for correlating chemical structures of natural products with the domains and modules in the corresponding nonribosomal peptide synthetases or polyketide synthases. It also provides guidelines for domain/module swapping as well as site-directed mutagenesis experiments to engineer biosynthesis of novel natural products. NRPS-PKS can be accessed at http://www.nii.res.in/nrps-pks.html.

Databases, Protein↗

PubMed2005

Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: a case study on renal function evaluation.

OBJECTIVE: In many medical areas, there exist different regression formulas to predict/evaluate a medical outcome on the same problem, each of them being efficient only in a particular sub-space of the problem space. The paper aims at the development of a generic, incremental learning model that includes all available regression formulas for a particular prediction problem to define local areas of the problem space with their best performing formula along with useful explanation rules. Another objective of the paper is to develop a specific model for renal function evaluation using nine existing formulas. METHODS AND MATERIALS: We have used a connectionist neuro-fuzzy approach and have developed a knowledge-based neural network model (KBNN) which incorporates and adapts incrementally several existing regression formulas and kernel functions. The model incorporates different non-linear regression functions as neurons in its hidden layer and adapts these functions through incremental learning from data in particular local areas of the space. More specifically, each hidden neural node has a pair of functions associated with it--one regression formula, that represents existing knowledge and one Gaussian kernel function, that defines the sub-space of the whole problem space, in which the formula is locally adapted to new data. All these functions are aggregated and changed through incremental learning. The proposed KBNN model is illustrated using a medical dataset of observed patient glomerular filtration rate (GFR) measurements for renal function evaluation. In this case study, the regression function for each cluster is selected by the model from nine formulas commonly used by medical practitioners to predict GFR. 441 GFR data vectors from 141 patients taken from 12 sites in Australia and New Zealand have been used as a case study experimental data set. RESULTS: The proposed GFR prediction model, based on the proposed generic KBNN model, outperforms at least by 10% accuracy any of the individual regression formulas or a standard neural network model. Furthermore, we have derived locally adapted regression formulas to perform best on local clusters of data along with useful explanatory rules. CONCLUSION: The proposed KBNN model manifests better accuracy then existing regression formulas or neural network models for renal function evaluation and extracts modified formulas that perform well in local areas of the problem space.

Algorithms↗

PubMed1993

A knowledge based interpretation system for EMG abnormalities.

The conventional method of diagnosis in electromyography is complex and time consuming, not only due to the large number of parameters, to be considered for diagnosis, but also because of the usual procedure of evaluating the different parameters of EMG signal by visual scanning of the plotted signal. So there is a clear need to make use of computer aided decision support system. In the present work an attempt has been made in the direction of integration into one automated system, the qualitative knowledge of the physician, with possibly sophisticated signal analysis tools which must replace the visual scanning. A software program (in Turbo-C) on a PC-AT has been developed to evaluate the different parameters of MUAP's (motor unit action potential) in a EMG signal. Then an Expert system (in Turbo-Prolog) has been implemented for diagnostic purposes of different muscular abnormalities by making a knowledge base from the different parameters involved in the decision making procedure of clinical electromyography. A hybrid model of rule and frame based Expert system is implemented. An attempt has been made for making a complete system, i.e., for recording, analysis and decision making for diagnosis.

Diagnosis, Computer-Assisted↗

PubMed1987

Knowledge-based decision support for general practitioners: an integrated design.

Most decision-support systems (DSSs) in medicine have been developed in hospital environments, for use in hospitals. Only a few are designed for use by general practitioners (GPs) in primary care. The work reported in this paper aims to (a) design DSSs for GPs in primary care, taking into consideration that primary care is the first level in a health care organization, where the reasons for the patients attendance are seen in a social context; and (b) integrate three approaches-Hypertext, Knowledge-Based Systems and Online Library-since any one of these will not suffice for the varying needs of GPs.

Academic Medical Centers↗

PubMed1999

kPROT: a knowledge-based scale for the propensity of residue orientation in transmembrane segments. Application to membrane protein structure prediction.

Modeling of integral membrane proteins and the prediction of their functional sites requires the identification of transmembrane (TM) segments and the determination of their angular orientations. Hydrophobicity scales predict accurately the location of TM helices, but are less accurate in computing angular disposition. Estimating lipid-exposure propensities of the residues from statistics of solved membrane protein structures has the disadvantage of relying on relatively few proteins. As an alternative, we propose here a scale of knowledge-based Propensities for Residue Orientation in Transmembrane segments (kPROT), derived from the analysis of more than 5000 non-redundant protein sequences. We assume that residues that tend to be exposed to the membrane are more frequent in TM segments of single-span proteins, while residues that prefer to be buried in the transmembrane bundle interior are present mainly in multi-span TMs. The kPROT value for each residue is thus defined as the logarithm of the ratio of its proportions in single and multiple TM spans. The scale is refined further by defining it for three discrete sections of the TM segment; namely, extracellular, central, and intracellular. The capacity of the kPROT scale to predict angular helical orientation was compared to that of alternative methods in a benchmark test, using a diversity of multi-span alpha-helical transmembrane proteins with a solved 3D structure. kPROT yielded an average angular error of 41 degrees, significantly lower than that of alternative scales (62 degrees -68 degrees ). The new scale thus provides a useful general tool for modeling and prediction of functional residues in membrane proteins. A WWW server (http://bioinfo.weizmann.ac.il/kPROT) is available for automatic helix orientation prediction with kPROT.

Amino Acid Sequence↗