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PubMed2002

Chronic pain in land mine accident survivors in Cambodia and Kurdistan.

The aims of the study were to study chronic pain in land mine accident survivors, and to study the impact of trauma and trauma care parameters on chronic pain. The level of chronic pain was registered (patient-rated and by clinical examination) in 57 severely injured adult land mine accident survivors in Cambodia and Northern Iraq more than one year after the accident. As all study patients had been managed by a standardized trauma system, we could assess the impact of injury severity and primary trauma care on chronic pain. 64% of the study patients (n=36) had chronic pain syndromes (non-significant difference between the two countries). 68% of the amputees (19 out of 28) had phantom limb pain. Pre-injury trauma exposure, the severity of the actual trauma, and the quality of trauma care had no impact on end point chronic pain. In 85% of cases (n=48), the economic standing of the patients' family had deteriorated after the accident. Patient-rated loss of income correlated with the rate of chronic pain syndromes.

Adolescent↗
PubMedDate not supplied

Uniqueness of medical data mining.

This article addresses the special features of data mining with medical data. Researchers in other fields may not be aware of the particular constraints and difficulties of the privacy-sensitive, heterogeneous, but voluminous data of medicine. Ethical and legal aspects of medical data mining are discussed, including data ownership, fear of lawsuits, expected benefits, and special administrative issues. The mathematical understanding of estimation and hypothesis formation in medical data may be fundamentally different than those from other data collection activities. Medicine is primarily directed at patient-care activity, and only secondarily as a research resource; almost the only justification for collecting medical data is to benefit the individual patient. Finally, medical data have a special status based upon their applicability to all people; their urgency (including life-or-death); and a moral obligation to be used for beneficial purposes.

Computer Security↗
PubMedDate not supplied

Mining knowledge for HEp-2 cell image classification.

HEp-2 cells are used for the identification of antinuclear autoantibodies (ANAs). They allow for recognition of over 30 different nuclear and cytoplasmic patterns, which are given by upwards of 100 different autoantibodies. The identification of the patterns has recently been done manually by a human inspecting the slides with a microscope. In this paper, we present results on the analysis and classification of cells using image analysis and data mining techniques. Starting from a knowledge acquisition process with a human operator, we developed an image analysis and feature extraction algorithm. The collection of the dataset was done based on an expert's image reading and based on the automatic extracted features. A dataset containing 132 features for each entry was set up and given to a data mining algorithm to find out the relevant features among this large feature set and to construct the classification knowledge. The classifier was evaluated by cross validation. The results gave the expert new insights into the necessary features and the classification knowledge and show the feasibility of an automated inspection system.

Algorithms↗
PubMed2002

Monte Carlo simulations as a feasibility tool for non-metallic land-mine detection by thermal-neutron backscattering.

The use of Monte Carlo simulations is presented for modelling a simplified land-mine detector system with thermal neutron backscattering (TNB) analysis based on a 252Cf-neutron source. Different aspects and a variety of external conditions, related to localisation and identification of a buried object have been investigated. In particular, the influence of moisture in a formation has been assessed, as moisture can be a serious interference for hydrogen as an indicator for explosives. The results of sensitivity calculations confirm that land-mine detection methods, based on an analysis of TNB may be applicable in homogeneous formations with low porosity provided that pore-water remains <5% by weight. In dry limestone, the TNT-based explosives can be well distinguished from other hydrogen-rich materials, except wood. However, in dry siliciclastic sands TNT explosives and wood are distinguishable.

Californium↗
PubMed2005

Text-mining approaches in molecular biology and biomedicine.

Biomedical articles provide functional descriptions of bioentities such as chemical compounds and proteins. To extract relevant information using automatic techniques, text-mining and information-extraction approaches have been developed. These technologies have a key role in integrating biomedical information through analysis of scientific literature. In this article, important applications such as the identification of biologically relevant entities in free text and the construction of literature-based networks of protein-protein interactions will be introduced. Also, the use of text mining to aid the interpretation of microarray data and the analysis of pathology reports will be discussed. Finally, we will consider the recent evolution of this field and the efforts for community-based evaluations.

Biomedical Research↗
PubMed2005

Analyzing and mining image databases.

Image mining is the application of computer-based techniques that extract and exploit information from large image sets to support human users in generating knowledge from these sources. This review focuses on biomedical applications, in particular automated imaging at the cellular level. An image database is an interactive software application that combines data management, image analysis and visual data mining. The main characteristic of such a system is a layer that represents objects within an image, and that represents a large spectrum of quantitative and semantic object features. The image analysis needs to be adapted to each particular experiment, so 'end-user programming' will be desirable to make the technology more widely applicable.

Humans↗
PubMed2001

Mining time dependency patterns in clinical pathways.

Clinical pathways are widely adopted by many large hospitals around the world in order to provide high-quality patient treatment and reduce the length of hospital stay of each patient. The development of clinical pathways is a lengthy process, and may require the collaboration among physicians, nurses, and staffs in a hospital. However, the individual differences cause great variances in the execution of clinical pathways. It calls for a more dynamic and adaptive process to improve the performance of clinical pathways. This paper reports a data mining technique we have developed to discover the time dependency pattern of clinical pathways for managing brain stroke. The mining of time dependency pattern is to discover patterns of process execution sequences and to identify the dependent relation between activities in a majority of cases. By obtaining the time dependency patterns, we can predict the paths for new patients when he/she is admitted into a hospital; in turn, the health care procedure will be more effective and efficient.

Algorithms↗
PubMed2001

Data mining approach to policy analysis in a health insurance domain.

This study examined the characteristics of the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically, this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms, CHIAD (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) using the test set of 4588 beneficiaries and the training set of 13,689 beneficiaries. Contrary to the previous study, the CHIAD algorithm performed better than the logistic regression in predicting hypertension, and C5.0 had the lowest predictive power. In addition, the CHIAD algorithm and the association rule also provided the segment-specific information for the risk factors and target group that may be used in a policy analysis for hypertension management.

Algorithms↗
PubMed2002

Terminology-driven literature mining and knowledge acquisition in biomedicine.

In this paper we describe Tagged Information Management System (TIMS), an integrated knowledge management system for the domain of molecular biology and biomedicine, in which terminology-driven literature mining, knowledge acquisition (KA), knowledge integration (KI), and XML-based knowledge retrieval are combined using tag information management and ontology inference. The system integrates automatic terminology acquisition, term variation management, hierarchical term clustering, tag-based information extraction (IE), and ontology-based query expansion. TIMS supports introducing and combining different types of tags (linguistic and domain-specific, manual and automatic). Tag-based interval operations and a query language are introduced in order to facilitate KA and retrieval from XML documents. Through KA examples, we illustrate the way in which literature mining techniques can be utilised for knowledge discovery from documents.

Artificial Intelligence↗
PubMed2002

A knowledge-driven agent-centred framework for data mining in EMG.

In this paper, we present a multi-agent framework for data mining in electromyography. This application, based on a web interface, provides a set of functionalities allowing to manipulate 1000 medical cases and more than 25,000 neurological tests stored in a medical database. The aim is to extract medical information using data mining algorithms and to supply a knowledge base with pertinent information. The multi-agent platform gives the possibility to distribute the data management process between several autonomous entities. This framework provides a parallel and flexible data manipulation.

Data Collection↗
PubMed2002

Association analysis for quantitative traits by data mining: QHPM.

Previously, we have presented a data mining-based algorithmic approach to genetic association analysis, Haplotype Pattern Mining. We have now extended the approach with the possibility of analysing quantitative traits and utilising covariates. This is accomplished by using a linear model for measuring association. We present results with the extended version, QHPM, with simulated quantitative trait data. One data set was simulated with the population simulator package Populus, and another was obtained from GAW12. In the former, there were 2-3 underlying susceptibility genes for a trait, each with several ancestral disease mutations, and 1 or 2 environmental components. We show that QHPM is capable of finding the susceptibility loci, even when there is strong allelic heterogeneity and environmental effects in the disease models. The power of finding quantitative trait loci is dependent on the ascertainment scheme of the data: collecting the study subjects from both ends of the quantitative trait distribution is more effective than using unselected individuals or individuals ascertained based on disease status, but QHPM has good power to localize the genes even with unselected individuals. Comparison with quantitative trait TDT (QTDT) showed that QHPM has better localization accuracy when the gene effect is weak.

Chromosome Mapping↗
PubMed2004

Data mining parasite genomes.

The term 'data mining' can be used to describe any process where useful information is extracted from data with a large background of 'noise'. In the context of a genome project, several stages involve data mining. Amongst the sequence data, 'signals' need to be detected that indicate the presence of interesting features. Often this involves differentiating between transcribed and non-transcribed bases to predict coding regions. After detection, defining the roles of these sequences involves sifting through multiple lines of evidence. If these roles are accurately reflected in genome annotation, they can be used by researchers to frame queries and interrogate the data further.

Animals↗
PubMed2006

Improving sensitivity in shotgun proteomics using a peptide-centric database with reduced complexity: protease cleavage and SCX elution rules from data mining of MS/MS spectra.

Correct identification of a peptide sequence from MS/MS data is still a challenging research problem, particularly in proteomic analyses of higher eukaryotes where protein databases are large. The scoring methods of search programs often generate cases where incorrect peptide sequences score higher than correct peptide sequences (referred to as distraction). Because smaller databases yield less distraction and better discrimination between correct and incorrect assignments, we developed a method for editing a peptide-centric database (PC-DB) to remove unlikely sequences and strategies for enabling search programs to utilize this peptide database. Rules for unlikely missed cleavage and nontryptic proteolysis products were identified by data mining 11 849 high-confidence peptide assignments. We also evaluated ion exchange chromatographic behavior as an editing criterion to generate subset databases. When used to search a well-annotated test data set of MS/MS spectra, we found no loss of critical information using PC-DBs, validating the methods for generating and searching against the databases. On the other hand, improved confidence in peptide assignments was achieved for tryptic peptides, measured by changes in DeltaCN and RSP. Decreased distraction was also achieved, consistent with the 3-9-fold decrease in database size. Data mining identified a major class of common nonspecific proteolytic products corresponding to leucine aminopeptidase (LAP) cleavages. Large improvements in identifying LAP products were achieved using the PC-DB approach when compared with conventional searches against protein databases. These results demonstrate that peptide properties can be used to reduce database size, yielding improved accuracy and information capture due to reduced distraction, but with little loss of information compared to conventional protein database searches.

Amino Acid Sequence↗
PubMedDate not supplied

Database mining using soft computing techniques. An integrated neural network-fuzzy logic-genetic algorithm approach.

Two different soft computing (SC) techniques (a competitive learning neural network and an integrated neural network-fuzzy logic-genetic algorithm approach) are employed in the analysis of a database subset obtained from the Cambridge Structural Database. The chemical problem chosen for study is relevant to the relationship between various metric parameters in transition metal imido (LnMdNZ, Z = carbon-based substituent) complexes and the chemical consequences of such relationships. The SC techniques confirmed and quantified the suspected relationship between the metal-nitrogen bond length and the metal-nitrogen-substituent bond angle for transition metal imidos: increased metal-nitrogen-carbon angles correlate with shortened metal-nitrogen distances. The mining effort also yielded an unexpected correlation between the NC distance and the MNC angle-shorter NC correlate with larger MNC. A fuzzy inference system is used to construct an MNred-NC-MNC hypersurface. This hypersurface suggests a complicated interdependence among NC, MNred, and the angle subtended by these two bonds. Also, major portions of the hypersurface are very flat, in regions where MNC is approaching linearity. The relationships are also seen to be influenced by whether the imido substituent is an alkyl or aryl group. Computationally, the present results are of particular interest in two respects. First, SC classification was able to isolate an "outlier" cluster. Identification of outliers is important as they may correspond to unreported experimental errors in the database or novel chemical entities, both of which warrant further investigation. Second, the SC database mining not only confirmed and quantified a suspected relationship (MNred versus MNC) within the data but also yielded a trend that was not suspected (NC versus MNC).

Journal Article↗
PubMed2004

Genome mining to identify new plant triterpenoids.

Arabidopsis thaliana is a well-established model organism for plant genetics, and its recently sequenced genome reveals a wealth of enzymes similar to known examples that biosynthesize secondary metabolites. We describe experiments that exploit this genomic information to identify novel terpenoids. A predicted open reading frame with high similarity to known oxidosqualene cyclases was shown to convert 3(S)-oxidosqualene to the previously unknown triterpene alcohol (3S,13S,14R)-malabarica-8,17,21-trien-3-ol, which we named thalianol. Genome mining offers a systematic approach to exhaustively characterize the biosynthetic potential of an organism, and is considerably more sensitive than classical approaches. Because even rare transcripts can be heterologously expressed at high levels, genome mining coupled to heterologous expression may be more sensitive than classical extraction approaches for isolating and characterizing trace metabolites.

Arabidopsis↗
PubMedDate not supplied

Clinical and pharmacogenomic data mining: 3. Zeta theory as a general tactic for clinical bioinformatics.

A new approach, a Zeta Theory of observations, data, and data mining, is being forged from a theory of expected information into an even more cohesive and comprehensive form by the challenge of general genomic, pharmacogenomic, and proteomic data. In this paper, the focus is not on studies using the specific tool FANO (CliniMiner) but on extensions to a new broader theoretical approach, aspects of which can easily be implemented into, or otherwise support, excellent existing methods, such as forms of multivariate analysis and IBM's product Intelligent Miner. The theory should perhaps be distinguished from an existing purely number-theoretic area sometimes also known as Zeta Theory, which focuses on the Riemann Zeta Function and the ways in which it governs the distribution of prime numbers. However, Zeta Theory as used here overlaps heavily with it and actually makes use of these same matters. The distinction is that it enters from a Bayesian information theory and data representation perspective. It could thus be considered an application of the 'mathematician's version'. The application is by no means confined to areas of modern biomedicine, and indeed its generality, even merging into quantum mechanics, is a key feature. Other areas with some similar challenges as modern biology, and which have inspired data mining methods such as IBM's Intelligent Miner, include commerce. But for several reasons discussed, modern molecular biology and medicine seem particularly challenging, and this relates to the often irreducible high dimensionality of the data. This thus remains our main target.

Computational Biology↗
PubMedDate not supplied

33,000-yr old chert mining site and related Homo in the Egyptian Nile Valley.

The Nazlet Khater 4 site (Nile Valley, Upper Egypt) is located on one of the small wadi- interfluves in the lower desert near the steep cliffs bordering the western Nile Valley edge (Fig. 1). We have previously reported the excavation of an early Upper Palaeolithic blade industry at this site, although blade industries in the Nile Valley had only been known to occur since 18,000 yr ago. The 1982 excavation reported here confirms that Nazlet Khater 4 is a chert mining site with a complex extraction strategy, going back 33,000 yr. A nearby grave contained a skeleton of a man in the extended position. We show that the cranial morphology is anatomically modern with archaic characteristics such as a very robust mandible. There is evidence that the skeleton has a similar age to that of the mining site.

Animals↗
PubMed2003

Damage to the shallow Landers fault from the nearby Hector Mine earthquake.

Crustal faults have long been identified as sites where localized sliding motion occurs during earthquakes, which allows for the relative motion between adjacent crustal blocks. Although there is a growing awareness that we must understand the evolution of fault systems on many timescales to relate present-day crustal stresses and fault motions to geological structures formed in the past, fault-zone damage and healing have been documented quantitatively in only a few cases. We have been monitoring the healing of damage on the shallow Johnson Valley fault after its rupture in the 1992 magnitude-7.3 Landers earthquake, and here we report that this healing was interrupted in 1999 by the magnitude-7.1 Hector Mine earthquake rupture, which occurred 20-30 km away. The Hector Mine earthquake both strongly shook and permanently strained the Johnson Valley fault, adding damage discernible as a temporary reversal of the healing process. The fault has since resumed the trend of strength recovery that it showed after the Landers earthquake. These observations lead us to speculate that fault damage caused by strong seismic waves may help to explain earthquake clustering and seismicity triggering by shaking, and may be involved in friction reduction during faulting.

Journal Article↗