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At least 775 records · Page 43Linked to original sources
PubMed2005

Application of validated QSAR models of D1 dopaminergic antagonists for database mining.

Rigorously validated quantitative structure-activity relationship (QSAR) models have been developed for 48 antagonists of the dopamine D1 receptor and applied to mining chemical datasets to discover novel potential antagonists. Several QSAR methods have been employed, including comparative molecular field analysis (CoMFA), simulated annealing-partial least squares (SA-PLS), k-nearest neighbor (kNN), and support vector machines (SVM). With the exception of CoMFA, these approaches employed 2D topological descriptors generated with the MolConnZ software package (EduSoft, LLC. MolconnZ, version 4.05; http://www.eslc.vabiotech.com/ [4.05], 2003). The original dataset was split into training and test sets to allow for external validation of each training set model. The resulting models were characterized by cross-validated R2 (q2) for the training set and predictive R2 values for the test set of (q2/R2) 0.51/0.47 for CoMFA, 0.7/0.76 for kNN, R2 for the training and test sets of 0.74/0.71 for SVM, and training set fitness and test set R2 values of 0.68/0.63 for SA-PLS. Validated QSAR models with R2 > 0.7, (i.e., kNN and SVM) were used to mine three publicly available chemical databases: the National Cancer Institute (NCI) database of ca. 250,000 compounds, the Maybridge Database of ca. 56,000 compounds, and the ChemDiv Database of ca. 450,000 compounds. These searches resulted in only 54 consensus hits (i.e., predicted active by all models); five of them were previously characterized as dopamine D1 ligands, but were not present in the original dataset. A small fraction of the purported D1 ligands did not contain a catechol ring found in all known dopamine full agonist ligands, suggesting that they may be novel structural antagonist leads. This study illustrates that the combined application of predictive QSAR modeling and database mining may provide an important avenue for rational computer-aided drug discovery.

Databases, Factual↗
PubMed2004

Genomic research and data-mining technology: implications for personal privacy and informed consent.

This essay examines issues involving personal privacy and informed consent that arise at the intersection of information and communication technology (ICT) and population genomics research. I begin by briefly examining the ethical, legal, and social implications (ELSI) program requirements that were established to guide researchers working on the Human Genome Project (HGP). Next I consider a case illustration involving deCODE Genetics, a privately owned genetic company in Iceland, which raises some ethical concerns that are not clearly addressed in the current ELSI guidelines. The deCODE case also illustrates some ways in which an ICT technique known as data mining has both aided and posed special challenges for researchers working in the field of population genomics. On the one hand, data-mining tools have greatly assisted researchers in mapping the human genome and in identifying certain "disease genes" common in specific populations (which, in turn, has accelerated the process of finding cures for diseases tha affect those populations). On the other hand, this technology has significantly threatened the privacy of research subjects participating in population genomics studies, who may, unwittingly, contribute to the construction of new groups (based on arbitrary and non-obvious patterns and statistical correlations) that put those subjects at risk for discrimination and stigmatization. In the final section of this paper I examine some ways in which the use of data mining in the context of population genomics research poses a critical challenge for the principle of informed consent, which traditionally has played a central role in protecting the privacy interests of research subjects participating in epidemiological studies.

Computational Biology↗
PubMed2004

Report generation and data mining in the domain of thoracic surgery.

As a part of AssistMe system, the reporting system has been developed for the thoracic surgery domain. Reporting System is defined as software for dynamic report generation purpose and based on the data-mining techniques. The target users of the future reporting system-physicians, administrative staff, and patients-have been identified. Two major types of clinical reports have been found: predefined and customized. The decision of splitting reports into groups has been taken mainly because users were heterogeneous and had different access rights to the sensitive information. Data-mining process in the reporting system is based on descriptive statistics. It allows dynamically mined AssistMe databases and generates statistical reports about patient's morbidity, mortality, and comorbidity. Information is visualized in the chart way and can be also observed in tabular form. User interaction is also supported by the system.

Heart-Assist Devices↗
PubMed2004

Flint mining in prehistory recorded by in situ-produced cosmogenic 10Be.

The development of mining to acquire the best raw materials for producing stone tools represents a breakthrough in human technological and intellectual development. We present a new approach to studying the history of flint mining, using in situ-produced cosmogenic 10Be concentrations. We show that the raw material used to manufacture flint artifacts approximately 300,000 years old from Qesem Cave (Israel) was most likely surface-collected or obtained from shallow quarries, whereas artifacts of the same period from Tabun Cave (Israel) were made of flint originating from layers 2 or more meters deep, possibly mined or quarried by humans.

Journal Article↗
PubMed2002

Dynamic assembly of MinD on phospholipid vesicles regulated by ATP and MinE.

Selection of the division site in Escherichia coli is regulated by the min system and requires the rapid oscillation of MinD between the two halves of the cell under the control of MinE. In this study we have further investigated the molecular basis for this oscillation by examining the interaction of MinD with phospholipid vesicles. We found that MinD bound to phospholipid vesicles in the presence of ATP and, upon binding, assembled into a well-ordered helical array that deformed the vesicles into tubes. Stimulation of the MinD ATPase by addition of MinE led to disassembly of the tubes and the release of MinD from the vesicles. It is proposed that this MinE-regulated dynamic assembly of MinD underlies MinD oscillation.

Adenosine Triphosphatases↗
PubMed2004

Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow.

The paper is illustrating how the general data mining methodology may be adapted to provide solutions to the problem of high throughput virtual screening of organic chemicals for possible acute toxicity to the fathead minnow fish. The present approach involves mining fragment information from chemical structures and is using probabilistic neural networks to model the relationship between structure and toxicity. Probabilistic neural networks implement a special class of multivariate non-linear Bayesian statistical models. The mathematical principles supporting their use for value prediction purposes are clarified and their peculiarities discussed. As part of the research phase of the data mining process, a dataset consisting of 800 structures and associated fathead minnow (Pimephales promelas) 96-h LC50 acute toxicity endpoint information is used for both the purpose of identifying an advantageous combination of fragment descriptors and for training the neural networks. As a result, two powerful models are generated. Model 1 implements the basic PNN with Gaussian kernel (statistical corrections included) while Model 2 implements the PNN with Gaussian kernel and separated variables. External validation is performed using a separate dataset consisting of 86 structures and associated toxicity information. Both learning and generalization capabilities of the two models are investigated and their limitations discussed.

Animals↗
PubMed2004

Use of a directional spray system design to control respirable dust and face gas concentrations around a continuous mining machine.

A laboratory study assessed the impacts of water spray pressure, face ventilation quantity, and line brattice setback distance on respirable dust and SF6 tracer gas concentrations around a continuous mining machine using a sprayfan or directional spray system. Dust levels were measured at locations representing the mining machine operator and the standard and off-standard shuttle car operators, and in the return airway. The results showed that changes in all three independent variables significantly affected log-transformed dust levels at the three operator sampling locations. Changes in setback distance impacted return airway dust levels. Laboratory testing also identified numerous variable interactions affecting dust levels. Tracer gas levels were measured on the left and right sides of the cutting drum and in the return. Untransformed gas levels around the cutting drum were significantly affected by changes in water pressure, face ventilation quantity, and setback distance. Only a few interactions were identified that significantly affected these concentrations. Gas levels in the return airway were grouped by face ventilation quantity. Return gas levels measured at the low curtain quantity were generally unaffected by changes in water pressure or curtain setback distance. At the high curtain quantity, return airway gas levels were affected by curtain setback distance. A field study was conducted to assess the impact of these parameters in an actual mining operation. These data showed that respirable dust levels may have been impacted by a change in water pressure and, to a lesser extent, by an increase in curtain setback distance. A series of tracer gas pulse tests were also conducted during this study. The results showed that effectiveness of the face ventilation was impacted by changes in curtain flow quantity and setback distance. Laboratory testing supported similar conclusions.

Caustics↗
PubMed2000

Natural radioactivity and radiation exposure at the Minjingu phosphate mine in Tanzania.

In this paper the results of studies on activity and ambient radiation background around the Minjingu phosphate mine in Tanzania are presented. The outdoor dose rate in air and the activity levels of samples from and outside the mine were determined by thermoluminiscent dosimeters and a gamma spectrometer system with a Hyper Pure germanium detector system respectively. The determination of activity was made for the 226Ra, 228Ra, 228Th and 40K radionuclides. High concentrations of radium-226 were observed in phosphate rock (5760+/-107 Bq kg(-1)), waste rock (4250+/-98 Bq kg(-1)), wild leaf vegetation (650+/-11 Bq kg(-1)), edible leaf vegetation (393+/-9 Bq kg(-1)), surface water (4.7+/-0.4 mBq l(-1)) and chicken feed (4+/-0.1 Bq kg(-1)) relative to selected control sites. These findings suggest a radiation health risk particularly when the samples are ingested, because the internal exposure may give rise to an effective dose exceeding 20 mSv which is the annual limit of intake of natural radionuclides recommended by the ICRP. On the other hand, the radiation dose from ambient air over five years at the phosphate mine ranges from 1375 to 1475 nGy h(-1) with an average of 1415 nGy h(-1). The average is about 28 times that of the global average background radiation from terrestrial sources, and about 12 times the allowed average dose limit for public exposure over five consecutive years. Future investigations on the occupancy factor, external dose rate and radon and radon progeny exposure in drinking water, buildings and activity content in the locally grown foodstuffs are proposed, for the realistic quantification of the overall exposure of workers and public at Minjingu, and remedial measures for future radiation safety.

Environmental Exposure↗
PubMed2003

Mining the biomedical literature in the genomic era: an overview.

The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of genomics and proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years, there has been a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature and find the nuggets of information most relevant and useful for specific analysis tasks. This paper provides a road map to the various literature-mining methods, both in general and within bioinformatics. It surveys the disciplines involved in unstructured-text analysis, categorizes current work in biomedical literature mining with respect to these disciplines, and provides examples of text analysis methods applied towards meeting some of the current challenges in bioinformatics.

Biomedical Research↗
PubMed2005

Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels.

There is a critical need for data-mining methods that can identify SNPs that predict among individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially fruitful form of exploratory data analysis is the Bayesian or Belief network. A Bayesian or Belief network provides an analytic approach for identifying robust predictors of among-individual variation in a disease endpoints or risk factor levels. We have applied Belief networks to SNP variation in the human APOE gene and plasma apolipoprotein E levels from two samples: 702 African-Americans from Jackson, MS, and 854 non-Hispanic whites from Rochester, MN. Twenty variable sites in the APOE gene were genotyped in both samples. In Jackson, MS, SNPs 4036 and 4075 were identified to influence plasma apoE levels. In Rochester, MN, SNPs 3937 and 4075 were identified to influence plasma apoE levels. All three SNPs had been previously implicated in affecting measures of lipid and lipoprotein metabolism. Like all data-mining methods, Belief networks are meant to complement traditional hypothesis-driven methods of data analysis. These results document the utility of a Belief network approach for mining large scale genotype-phenotype association data.

Apolipoproteins E↗
PubMed2003

Whole-proteome interaction mining.

MOTIVATION: A major post-genomic scientific and technological pursuit is to describe the functions performed by the proteins encoded by the genome. One strategy is to first identify the protein-protein interactions in a proteome, then determine pathways and overall structure relating these interactions, and finally to statistically infer functional roles of individual proteins. Although huge amounts of genomic data are at hand, current experimental protein interaction assays must overcome technical problems to scale-up for high-throughput analysis. In the meantime, bioinformatics approaches may help bridge the information gap required for inference of protein function. In this paper, a previously described data mining approach to prediction of protein-protein interactions (Bock and Gough, 2001, Bioinformatics, 17, 455-460) is extended to interaction mining on a proteome-wide scale. An algorithm (the phylogenetic bootstrap) is introduced, which suggests traversal of a phenogram, interleaving rounds of computation and experiment, to develop a knowledge base of protein interactions in genetically-similar organisms. RESULTS: The interaction mining approach was demonstrated by building a learning system based on 1,039 experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori. An estimate of the generalization performance of the classifier was derived from 10-fold cross-validation, which indicated expected upper bounds on precision of 80% and sensitivity of 69% when applied to related organisms. One such organism is the enteric pathogen Campylobacter jejuni, in which comprehensive machine learning prediction of all possible pairwise protein-protein interactions was performed. The resulting network of interactions shares an average protein connectivity characteristic in common with previous investigations reported in the literature, offering strong evidence supporting the biological feasibility of the hypothesized map. For inferences about complete proteomes in which the number of pairwise non-interactions is expected to be much larger than the number of actual interactions, we anticipate that the sensitivity will remain the same but precision may decrease. We present specific biological examples of two subnetworks of protein-protein interactions in C. jejuni resulting from the application of this approach, including elements of a two-component signal transduction systems for thermoregulation, and a ferritin uptake network.

Algorithms↗
PubMed2003

Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup.

MOTIVATION: The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases. Text mining may provide useful tools to assist in the curation process. To date, the lack of standards has made it impossible to determine whether text mining techniques are sufficiently mature to be useful. RESULTS: We report on a Challenge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup. We provided a training corpus of 862 articles consisting of journal articles curated in FlyBase, along with the associated lists of genes and gene products, as well as the relevant data fields from FlyBase. For the test, we provided a corpus of 213 new ('blind') articles; the 18 participating groups provided systems that flagged articles for curation, based on whether the article contained experimental evidence for gene expression products. We report on the evaluation results and describe the techniques used by the top performing groups.

Abstracting and Indexing↗
PubMed2003

Terminology-driven mining of biomedical literature.

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective literature mining techniques that can help biologists to gather and make use of the knowledge encoded in text documents. Although the knowledge is organized around sets of domain-specific terms, few literature mining systems incorporate deep and dynamic terminology processing. RESULTS: In this paper, we present an overview of an integrated framework for terminology-driven mining from biomedical literature. The framework integrates the following components: automatic term recognition, term variation handling, acronym acquisition, automatic discovery of term similarities and term clustering. The term variant recognition is incorporated into terminology recognition process by taking into account orthographical, morphological, syntactic, lexico-semantic and pragmatic term variations. In particular, we address acronyms as a common way of introducing term variants in biomedical papers. Term clustering is based on the automatic discovery of term similarities. We use a hybrid similarity measure, where terms are compared by using both internal and external evidence. The measure combines lexical, syntactical and contextual similarity. Experiments on terminology recognition and clustering performed on a corpus of MEDLINE abstracts recorded the precision of 98 and 71% respectively. AVAILABILITY: software for the terminology management is available upon request.

Abbreviations as Topic↗
PubMed2004

NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis.

SUMMARY: The NetAffx Gene Ontology (GO) Mining Tool is a web-based, interactive tool that permits traversal of the GO graph in the context of microarray data. It accepts a list of Affymetrix probe sets and renders a GO graph as a heat map colored according to significance measurements. The rendered graph is interactive, with nodes linked to public web sites and to lists of the relevant probe sets. The GO Mining Tool provides visualization combining biological annotation with expression data, encompassing thousands of genes in one interactive view. AVAILABILITY: GO Mining Tool is freely available at http://www.affymetrix.com/analysis/query/go_analysis.affx

Abstracting and Indexing↗
PubMed2004

Mining MEDLINE for implicit links between dietary substances and diseases.

MOTIVATION: Text mining systems aim at knowledge discovery from text collections. This work presents our text mining algorithm and demonstrates its use to uncover information that could form the basis of new hypotheses. In particular, we use it to discover novel uses for Curcuma longa, a dietary substance, which is highly regarded for its therapeutic properties in Asia. RESULTS: Several disease were identified that offer novel research contexts for curcumin. We analyze select suggestions, such as retinal diseases, Crohn's disease and disorders related to the spinal cord. Our analysis suggests that there is strong evidence in favor of a beneficial role for curcumin in these diseases. The evidence is based on curcumin's influence on several genes, such as COX-2, TNF-alpha, JNK, p38 MAPK and TGF-beta. This research suggests that our discovery algorithm may be used to suggest novel uses for dietary and pharmacological substances. More generally, our text mining algorithm may be used to uncover information that potentially sheds new light on a given topic of interest. AVAILABILITY: Contact authors.

Abstracting and Indexing↗
PubMed2004

MedKit: a helper toolkit for automatic mining of MEDLINE/PubMed citations.

UNLABELLED: MEDLINE/PubMed is one of the most important information sources for bioinformatics text mining. However, there remain limitations in working with MEDLINE/PubMed citations. For example, PubMed imposes an upper limit of 10,000 for downloading PMID list or citations; and MEDLINE files are too large for most off-the-shelf XML parsers. We developed a Java package, MedKit, to work-around the limitations, as well as provide other useful functionalities, e.g. random sampling. Its four modules (querier, sampler, fetcher and parser) can work independently, or be pipelined in various combinations. It can be used as a stand-alone GUI application, or integrated into other text-mining systems. Text mining researchers and others may download and use the toolkit free for non-commercial purposes. AVAILABILITY: http://metnetdb.gdcb.iastate.edu/medkit CONTACT: berleant@iastate.edu.

Abstracting and Indexing↗
PubMed2005

Literature mining and database annotation of protein phosphorylation using a rule-based system.

MOTIVATION: A large volume of experimental data on protein phosphorylation is buried in the fast-growing PubMed literature. While of great value, such information is limited in databases owing to the laborious process of literature-based curation. Computational literature mining holds promise to facilitate database curation. RESULTS: A rule-based system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was used to extract protein phosphorylation information from MEDLINE abstracts. An annotation-tagged literature corpus developed at PIR was used to evaluate the system for finding phosphorylation papers and extracting phosphorylation objects (kinases, substrates and sites) from abstracts. RLIMS-P achieved a precision and recall of 91.4 and 96.4% for paper retrieval, and of 97.9 and 88.0% for extraction of substrates and sites. Coupling the high recall for paper retrieval and high precision for information extraction, RLIMS-P facilitates literature mining and database annotation of protein phosphorylation.

Abstracting and Indexing↗
PubMed2006

An online literature mining tool for protein phosphorylation.

A web-based version of the RLIMS-P literature mining system was developed for online mining of protein phosphorylation information from MEDLINE abstracts. The online tool presents extracted phosphorylation objects (phosphorylated proteins, phosphorylation sites and protein kinases) in summary tables and full reports with evidence-tagged abstracts. The tool further allows mapping of phosphorylated proteins to protein entries in the UniProt Knowledgebase based on PubMed ID and/or protein name. The literature mining, coupled with database association, allows retrieval of rich biological information for the phosphorylated proteins and facilitates database annotation of phosphorylation features.

Animals↗