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PubMed2004

Confirmation of data mining based predictions of protein function.

MOTIVATION: A central problem in bioinformatics is the assignment of function to sequenced open reading frames (ORFs). The most common approach is based on inferred homology using a statistically based sequence similarity (SIM) method, e.g. PSI-BLAST. Alternative non-SIM based bioinformatic methods are becoming popular. One such method is Data Mining Prediction (DMP). This is based on combining evidence from amino-acid attributes, predicted structure and phylogenic patterns; and uses a combination of Inductive Logic Programming data mining, and decision trees to produce prediction rules for functional class. DMP predictions are more general than is possible using homology. In 2000/1, DMP was used to make public predictions of the function of 1309 Escherichia coli ORFs. Since then biological knowledge has advanced allowing us to test our predictions. RESULTS: We examined the updated (20.02.02) Riley group genome annotation, and examined the scientific literature for direct experimental derivations of ORF function. Both tests confirmed the DMP predictions. Accuracy varied between rules, and with the detail of prediction, but they were generally significantly better than random. For voting rules, accuracies of 75-100% were obtained. Twenty-one of these DMP predictions have been confirmed by direct experimentation. The DMP rules also have interesting biological explanations. DMP is, to the best of our knowledge, the first non-SIM based prediction method to have been tested directly on new data. AVAILABILITY: We have designed the "Genepredictions" database for protein functional predictions. This is intended to act as an open repository for predictions for any organism and can be accessed at http://www.genepredictions.org

Abstracting and Indexing↗

PubMed2004

Mining gene expression data for positive and negative co-regulated gene clusters.

MOTIVATION: Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs. RESULTS: We experimented with the Yeast dataset and compared our resulting PNCGCs with the association rules generated by the Apriori mining algorithm. Our results show that our PNCGCs identify some missing co-regulations of association rules, and our algorithm greatly reduces the large number of rules involving uncorrelated genes generated by the Apriori scheme. AVAILABILITY: The software is available upon request.

Algorithms↗

PubMed2004

MyWEST: my Web Extraction Software Tool for effective mining of annotations from web-based databanks.

MOTIVATION: High-throughput technologies create the necessity to mine large amounts of gene annotations from diverse databanks, and to integrate the resulting data. Most databanks can be interrogated only via Web, for a single gene at a time, and query results are generally available only in the HTML format. Although some databanks provide batch retrieval of data via FTP, this requires expertise and resources for locally reimplementing the databank. RESULTS: We developed MyWEST, a tool aimed at researchers without extensive informatics skills or resources, which exploits user-defined templates to easily mine selected annotations from different Web-interfaced databanks, and aggregates and structures results in an automatically updated database. Using microarray results from a model system of retinoic acid-induced differentiation, MyWEST effectively gathered relevant annotations from various biomolecular databanks, highlighted significant biological characteristics and supported a global approach to the understanding of complex cellular mechanisms. AVAILABILITY: MyWEST is freely available for non-profit use at http://www.medinfopoli.polimi.it/MyWEST/

Algorithms↗

PubMed2004

MedlineR: an open source library in R for Medline literature data mining.

SUMMARY: We describe an open source library written in the R programming language for Medline literature data mining. This MedlineR library includes programs to query Medline through the NCBI PubMed database; to construct the co-occurrence matrix; and to visualize the network topology of query terms. The open source nature of this library allows users to extend it freely in the statistical programming language of R. To demonstrate its utility, we have built an application to analyze term-association by using only 10 lines of code. We provide MedlineR as a library foundation for bioinformaticians and statisticians to build more sophisticated literature data mining applications. AVAILABILITY: The library is available from http://dbsr.duke.edu/pub/MedlineR.

Abstracting and Indexing↗

PubMed2006

Annotating proteins by mining protein interaction networks.

MOTIVATION: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins. RESULTS: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%. AVAILABILITY: Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN.

Amino Acid Sequence↗

PubMed2001

SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics.

High-throughput structural proteomics is expected to generate considerable amounts of data on the progress of structure determination for many proteins. For each protein this includes information about cloning, expression, purification, biophysical characterization and structure determination via NMR spectroscopy or X-ray crystallography. It will be essential to develop specifications and ontologies for standardizing this information to make it amenable to retrospective analysis. To this end we created the SPINE database and analysis system for the Northeast Structural Genomics Consortium. SPINE, which is available at bioinfo.mbb.yale.edu/nesg or nesg.org, is specifically designed to enable distributed scientific collaboration via the Internet. It was designed not just as an information repository but as an active vehicle to standardize proteomics data in a form that would enable systematic data mining. The system features an intuitive user interface for interactive retrieval and modification of expression construct data, query forms designed to track global project progress and external links to many other resources. Currently the database contains experimental data on 985 constructs, of which 740 are drawn from Methanobacterium thermoautotrophicum, 123 from Saccharomyces cerevisiae, 93 from Caenorhabditis elegans and the remainder from other organisms. We developed a comprehensive set of data mining features for each protein, including several related to experimental progress (e.g. expression level, solubility and crystallization) and 42 based on the underlying protein sequence (e.g. amino acid composition, secondary structure and occurrence of low complexity regions). We demonstrate in detail the application of a particular machine learning approach, decision trees, to the tasks of predicting a protein's solubility and propensity to crystallize based on sequence features. We are able to extract a number of key rules from our trees, in particular that soluble proteins tend to have significantly more acidic residues and fewer hydrophobic stretches than insoluble ones. One of the characteristics of proteomics data sets, currently and in the foreseeable future, is their intermediate size ( approximately 500-5000 data points). This creates a number of issues in relation to error estimation. Initially we estimate the overall error in our trees based on standard cross-validation. However, this leaves out a significant fraction of the data in model construction and does not give error estimates on individual rules. Therefore, we present alternative methods to estimate the error in particular rules.

Animals↗

PubMed2005

Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

Anatomy↗

PubMed2005

Data mining tools for the Saccharomyces cerevisiae morphological database.

For comprehensive understanding of precise morphological changes resulting from loss-of-function mutagenesis, a large collection of 1,899,247 cell images was assembled from 91,71 micrographs of 4782 budding yeast disruptants of non-lethal genes. All the cell images were processed computationally to measure approximately 500 morphological parameters in individual mutants. We have recently made this morphological quantitative data available to the public through the Saccharomyces cerevisiae Morphological Database (SCMD). Inspecting the significance of morphological discrepancies between the wild type and the mutants is expected to provide clues to uncover genes that are relevant to the biological processes producing a particular morphology. To facilitate such intensive data mining, a suite of new software tools for visualizing parameter value distributions was developed to present mutants with significant changes in easily understandable forms. In addition, for a given group of mutants associated with a particular function, the system automatically identifies a combination of multiple morphological parameters that discriminates a mutant group from others significantly, thereby characterizing the function effectively. These data mining functions are available through the World Wide Web at http://scmd.gi.k.u-tokyo.ac.jp/.

Computer Graphics↗

PubMedDate not supplied

Data mining as a tool for research and knowledge development in nursing.

The ability to collect and store data has grown at a dramatic rate in all disciplines over the past two decades. Healthcare has been no exception. The shift toward evidence-based practice and outcomes research presents significant opportunities and challenges to extract meaningful information from massive amounts of clinical data to transform it into the best available knowledge to guide nursing practice. Data mining, a step in the process of Knowledge Discovery in Databases, is a method of unearthing information from large data sets. Built upon statistical analysis, artificial intelligence, and machine learning technologies, data mining can analyze massive amounts of data and provide useful and interesting information about patterns and relationships that exist within the data that might otherwise be missed. As domain experts, nurse researchers are in ideal positions to use this proven technology to transform the information that is available in existing data repositories into useful and understandable knowledge to guide nursing practice and for active interdisciplinary collaboration and research.

Algorithms↗

PubMed2003

EST mining and functional expression assays identify extracellular effector proteins from the plant pathogen Phytophthora.

Plant pathogenic microbes have the remarkable ability to manipulate biochemical, physiological, and morphological processes in their host plants. These manipulations are achieved through a diverse array of effector molecules that can either promote infection or trigger defense responses. We describe a general functional genomics approach aimed at identifying extracellular effector proteins from plant pathogenic microorganisms by combining data mining of expressed sequence tags (ESTs) with virus-based high-throughput functional expression assays in plants. PexFinder, an algorithm for automated identification of extracellular proteins from EST data sets, was developed and applied to 2147 ESTs from the oomycete plant pathogen Phytophthora infestans. The program identified 261 ESTs (12.2%) corresponding to a set of 142 nonredundant Pex (Phytophthora extracellular protein) cDNAs. Of these, 78 (55%) Pex cDNAs were novel with no significant matches in public databases. Validation of PexFinder was performed using proteomic analysis of secreted protein of P. infestans. To identify which of the Pex cDNAs encode effector proteins that manipulate plant processes, high-throughput functional expression assays in plants were performed on 63 of the identified cDNAs using an Agrobacterium tumefaciens binary vector carrying the potato virus X (PVX) genome. This led to the discovery of two novel necrosis-inducing cDNAs, crn1 and crn2, encoding extracellular proteins that belong to a large and complex protein family in Phytophthora. Further characterization of the crn genes indicated that they are both expressed in P. infestans during colonization of the host plant tomato and that crn2 induced defense-response genes in tomato. Our results indicate that combining data mining using PexFinder with PVX-based functional assays can facilitate the discovery of novel pathogen effector proteins. In principle, this strategy can be applied to a variety of eukaryotic plant pathogens, including oomycetes, fungi, and nematodes.

Algal Proteins↗

PubMed2004

Association rule mining in peer-to-peer systems.

We extend the problem of association rule mining--a key data mining problem--to systems in which the database is partitioned among a very large number of computers that are dispersed over a wide area. Such computing systems include grid computing platforms, federated database systems, and peer-to-peer computing environments. The scale of these systems poses several difficulties, such as the impracticality of global communications and global synchronization, dynamic topology changes of the network, on-the-fly data updates, the need to share resources with other applications, and the frequent failure and recovery of resources. We present an algorithm by which every node in the system can reach the exact solution, as if it were given the combined database. The algorithm is entirely asynchronous, imposes very little communication overhead, transparently tolerates network topology changes and node failures, and quickly adjusts to changes in the data as they occur. Simulation of up to 10,000 nodes show that the algorithm is local: all rules, except for those whose confidence is about equal to the confidence threshold, are discovered using information gathered from a very small vicinity, whose size is independent of the size of the system.

Algorithms↗

PubMed2005

Evolutionary optimization of radial basis function classifiers for data mining applications.

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Algorithms↗

PubMed2003

Evolutionary dynamics of host-plant use in a genus of leaf-mining moths.

We used nuclear 28S rDNA sequence data to estimate the phylogeny of 77 leaf-mining Phyllonorycter (Gracillariidae) moth species, including all 55 British species, feeding on 44 different plant genera. There was strong support for both the monophyly of Phyllonorycter and the placement of the genus Cameraria as its sister group. Host-plant use was mapped onto the moth phylogeny and investigated statistically in several ways. First, we show that the estimated level of cospeciation between leaf miners and their host plants is not greater than expected by chance, despite the physical intimacy of the association. Nevertheless, the pattern of host-plant use is far from random, with closely related Phyllonorycter species generally feeding on closely related plants. However, although Phyllonorycter species from a given host plant tend to form distinct clades, there is also statistical support for multiple independent colonizations of some host-plant taxa (e.g. the order Rosales and the genus Corylus). Despite numerous host shifts, most Phyllonorycter species feed on trees and the few species that attack shrubs or herbs have mostly acquired these habits independently. There is also limited evidence that host shifts to herbs are more likely from shrubs than from trees. Similarly, most species mine the lower surface of leaves but the few upper-surface miners have each evolved the habit independently. Consequently, these shifts to new adaptive zones have not led to substantial radiations.

Animals↗

PubMed2003

Integration of different data bodies for humanitarian decision support: an example from mine action.

Geographic information systems (GIS) are increasingly used for integrating data from different sources and substantive areas, including in humanitarian action. The challenges of integration are particularly well illustrated by humanitarian mine action. The informational requirements of mine action are expensive, with socio-economic impact surveys costing over US$1.5 million per country, and are feeding a continuous debate on the merits of considering more factors or 'keeping it simple'. National census offices could, in theory, contribute relevant data, but in practice surveys have rarely overcome institutional obstacles to external data acquisition. A positive exception occurred in Lebanon, where the landmine impact survey had access to agricultural census data. The challenges, costs and benefits of this data integration exercise are analysed in a detailed case study. The benefits are considerable, but so are the costs, particularly the hidden ones. The Lebanon experience prompts some wider reflections. In the humanitarian community, data integration has been fostered not only by the diffusion of GIS technology, but also by institutional changes such as the creation of UN-led Humanitarian Information Centres. There is a question whether the analytic capacity is in step with aggressive data acquisition. Humanitarian action may yet have to build the kind of strong analytic tradition that public health and poverty alleviation have accomplished.

Agriculture↗

PubMed2006

Fossil-calibrated molecular phylogenies reveal that leaf-mining moths radiated millions of years after their host plants.

Coevolution has been hypothesized as the main driving force for the remarkable diversity of insect-plant associations. Dating of insect and plant phylogenies allows us to test coevolutionary hypotheses and distinguish between the contemporaneous radiation of interacting lineages vs. insect 'host tracking' of previously diversified plants. Here, we used nuclear DNA to reconstruct a molecular phylogeny for 100 species of Phyllonorycter leaf-mining moths and 36 outgroup taxa. Ages for nodes in the moth phylogeny were estimated using a combination of a penalized likelihood method and a Bayesian approach, which takes into account phylogenetic uncertainty. To convert the relative ages of the moths into dates, we used an absolute calibration point from the fossil record. The age estimates of (a selection of) moth clades were then compared with fossil-based age estimates of their host plants. Our results show that the principal radiation of Phyllonorycter leaf-mining moths occurred well after the main radiation of their host plants and may represent the dominant associational mode in the fossil record.

Animals↗

PubMed2001

Mantle flow beneath a continental strike-slip fault: postseismic deformation after the 1999 Hector Mine earthquake.

Two recent large earthquakes in the Mojave Desert, California-the magnitude 7.3 1992 Landers and magnitude 7.1 1999 Hector Mine earthquakes-have each been followed by elevated crustal strain rates over periods of months and years. Geodetic data collected after the Hector Mine earthquake exhibit a temporally decaying horizontal velocity field and a quadrant uplift pattern opposite to that expected for localized shear beneath the earthquake rupture. We interpret the origin of this accelerated crustal deformation to be vigorous flow in the upper mantle in response to the stress changes generated by the earthquake. Our results suggest that transient flow in the upper mantle is a fundamental component of the earthquake cycle and that the lower crust is a coherent stress guide coupling the upper crust with the upper mantle.

Journal Article↗

PubMed2005

Text mining for metabolic pathways, signaling cascades, and protein networks.

The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

Animals↗

PubMed1983

Effect of microorganisms on in situ uranium mining.

The extraction of some metal values, e.g., uranium or copper, may be accomplished by using solutions to remove metals from ore bodies without practicing conventional mining. This process is referred to as in situ leaching and has been used industrially to recover uranium. The growth of microbial populations during in situ leaching is believed to be one of the causes of flow path plugging in the ore body, which results in decreased uranium production. Leach solution and solid samples from well casings and submersible pumps were collected from an in situ mining operation experiencing plugging problems. Bacillus sp., Micrococcus sp., pseudomonads, and xanthomonads were isolated from these samples in concentrations of 10 CFU ml. A mixed culture of these organisms was inoculated into a uranium core specimen in the laboratory to assess the role of microbes in the plugging problem. A one-third decrease in permeability was effected in 16 days. Hydrogen peroxide (0.2 g liter) killed the microorganisms in the core and alleviated the plugging problem. Periodically injecting hydrogen peroxide into the ore body through the production wells may reduce microbial plugging problems.

Journal Article↗