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PubMed1984

Growth Kinetics of Thiobacillus ferrooxidans Isolated from Arsenic Mine Drainage.

Thiobacillus ferrooxidans is found in many Alaskan and Canadian drainages contaminated by metals dissolved from placer and lode gold mines. We have examined the iron-limited growth and iron oxidation kinetics of a T. ferrooxidans isolate, AK1, by using batch and continuous cultures. Strain AK1 is an arsenic-tolerant isolate obtained from placer gold mine drainage containing large amounts of dissolved arsenic. The steady-state growth kinetics are described with equations modified for threshold ferrous iron concentrations. The maximal specific growth rate (mu(max)) for isolate AK1 at 22.5 degrees C was 0.070 h, and the ferrous iron concentration at which the half-maximal growth rate occurred (K(mu)) was 0.78 mM. Cell yields varied inversely with growth rate. The iron oxidation kinetics of this organism were dependent on biomass. We found no evidence of ferric inhibition of ferrous iron oxidation for ferrous iron concentrations between 9.0 and 23.3 mM. A supplement to the ferrous medium of 2.67 mM sodium arsenite did not result in an increased steady-state biomass, nor did it appear to affect the steady-state growth kinetics observed in continuous cultures.

Journal Article↗
PubMed1988

Metabolic processes involved in repair of Escherichia coli cells damaged by exposure to acid mine water.

Escherichia coli was stressed by exposure to filter-sterilized acid mine water. Synthetic processes required for repair of sublethally injured survivors were studied by the addition of specific metabolic inhibitors to a resuscitation broth. Inhibitors of protein, RNA, DNA, lipid, and peptidoglycan synthesis as well as uncouplers and inhibitors of electron transport and ATPase activity were used. Acid mine water injury was severe, causing damage to the outer and cytoplasmic membranes. Repair of sublethally injured cells required protein, RNA, and lipid synthesis as well as a proton motive force.

2,4-Dinitrophenol↗
PubMed1995

Microbial diversity in uranium mine waste heaps.

Two different uranium mine waste heaps near Ronneburg, Thuringia, Germany, which contain the remains of the activity of the former uranium-mining Soviet-East German company Wismut AG, were analyzed for the occurrence of lithotrophic and chemoorganotropic leach bacteria. A total of 162 ore samples were taken up to a depth of 5 m. Cell counts of ferrous iron-, sulfur-, sulfur compound-, ammonia-, and nitrite-oxidizing bacteria were determined quantitatively by the most-probable-number technique. Sulfate-, nitrate-, ferric iron-, and manganese-reducing bacteria were also detected. In addition, the metabolic activity of sulfur- and iron-oxidizing bacteria was measured by microcalorimetry. Generally, all microorganisms mentioned above were detectable in the heaps. Aerobic and anaerobic microorganisms thrived up to a depth of 1.5 to 2 m. Up to 99% of Thiobacillus ferrooxidans cells, the dominant leaching bacteria, occurred to this depth. Their numbers correlated with the microbial activity measurements. Samples below 1.5 to 2 m exhibited reduced oxygen concentrations and reduced cell counts for all microorganisms.

Journal Article↗
PubMed1999

Microbiological and geochemical characterization of fluvially deposited sulfidic mine tailings

The fluvial deposition of mine tailings generated from historic mining operations near Butte, Montana, has resulted in substantial surface and shallow groundwater contamination along Silver Bow Creek. Biogeochemical processes in the sediment and underlying hyporheic zone were studied in an attempt to characterize interactions consequential to heavy-metal contamination of shallow groundwater. Sediment cores were extracted and fractionated based on sediment stratification. Subsamples of each fraction were assayed for culturable heterotrophic microbiota, specific microbial guilds involved in metal redox transformations, and both aqueous- and solid-phase geochemistry. Populations of cultivable Fe(III)-reducing bacteria were most prominent in the anoxic, circumneutral pH regions associated with a ferricrete layer or in an oxic zone high in organic carbon and soluble iron. Sulfur- and iron-oxidizing bacteria were distributed in discrete zones throughout the tailings and were often recovered from sections at and below the anoxic groundwater interface. Sulfate-reducing bacteria were also widely distributed in the cores and often occurred in zones overlapping iron and sulfur oxidizers. Sulfate-reducing bacteria were consistently recovered from oxic zones that contained high concentrations of metals in the oxidizable fraction. Altogether, these results suggest a highly varied and complex microbial ecology within a very heterogeneous geochemical environment. Such physical and biological heterogeneity has often been overlooked when remediation strategies for metal contaminated environments are formulated.

Journal Article↗
PubMedDate not supplied

Association rules and data mining in hospital infection control and public health surveillance.

OBJECTIVES: The authors consider the problem of identifying new, unexpected, and interesting patterns in hospital infection control and public health surveillance data and present a new data analysis process and system based on association rules to address this problem. DESIGN: The authors first illustrate the need for automated pattern discovery and data mining in hospital infection control and public health surveillance. Next, they define association rules, explain how those rules can be used in surveillance, and present a novel process and system--the Data Mining Surveillance System (DMSS)--that utilize association rules to identify new and interesting patterns in surveillance data. RESULTS: Experimental results were obtained using DMSS to analyze Pseudomonas aeruginosa infection control data collected over one year (1996) at University of Alabama at Birmingham Hospital. Experiments using one-, three-, and six-month time partitions yielded 34, 57, and 28 statistically significant events, respectively. Although not all statistically significant events are clinically significant, a subset of events generated in each analysis indicated potentially significant shifts in the occurrence of infection or antimicrobial resistance patterns of P. aeruginosa. CONCLUSION: The new process and system are efficient and effective in identifying new, unexpected, and interesting patterns in surveillance data. The clinical relevance and utility of this process await the results of prospective studies currently in progress.

Data Interpretation, Statistical↗
PubMed2002

Mining MEDLINE: abstracts, sentences, or phrases?

A growing body of works address automated mining of biochemical knowledge from digital repositories of scientific literature, such as MEDLINE. Some of these works use abstracts as the unit of text from which to extract facts. Others use sentences for this purpose, while still others use phrases. Here we compare abstracts, sentences, and phrases in MEDLINE using the standard information retrieval performance measures of recall, precision, and effectiveness, for the task of mining interactions among biochemical terms based on term co-occurrence. Results show statistically significant differences that can impact the choice of text unit.

Abstracting and Indexing↗
PubMed2005

Corticosteroid-regulated genes in rat kidney: mining time series array data.

Kidney is a major target for adverse effects associated with corticosteroids. A microarray dataset was generated to examine changes in gene expression in rat kidney in response to methylprednisolone. Four control and 48 drug-treated animals were killed at 16 times after drug administration. Kidney RNA was used to query 52 individual Affymetrix chips, generating data for 15,967 different probe sets for each chip. Mining techniques applicable to time series data that identify drug-regulated changes in gene expression were applied. Four sequential filters eliminated probe sets that were not expressed in the tissue, not regulated by drug, or did not meet defined quality control standards. These filters eliminated 14,890 probe sets (94%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series datasets. The remaining data can then be further analyzed by clustering and mathematical modeling. Initial analysis of this filtered dataset identified a group of genes whose pattern of regulation was highly correlated with prototype corticosteroid enhanced genes. Twenty genes in this group, as well as selected genes exhibiting either downregulation or no regulation, were analyzed for 5' GRE half-sites conserved across species. In general, the results support the hypothesis that the existence of conserved DNA binding sites can serve as an important adjunct to purely analytic approaches to clustering genes into groups with common mechanisms of regulation. This dataset, as well as similar datasets on liver and muscle, are available online in a format amenable to further analysis by others.

Animals↗
PubMed2006

Microarray analysis of gene expression: considerations in data mining and statistical treatment.

DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.

Cluster Analysis↗
PubMedDate not supplied

Novel approaches to visualization and data mining reveals diagnostic information in the low amplitude region of serum mass spectra from ovarian cancer patients.

The ability to identify patterns of diagnostic signatures in proteomic data generated by high throughput mass spectrometry (MS) based serum analysis has recently generated much excitement and interest from the scientific community. These data sets can be very large, with high-resolution MS instrumentation producing 1-2 million data points per sample. Approaches to analyze mass spectral data using unsupervised and supervised data mining operations would greatly benefit from tools that effectively allow for data reduction without losing important diagnostic information. In the past, investigators have proposed approaches where data reduction is performed by a priori "peak picking" and alignment/warping/smoothing components using rule-based signal-to-noise measurements. Unfortunately, while this type of system has been employed for gene microarray analysis, it is unclear whether it will be effective in the analysis of mass spectral data, which unlike microarray data, is comprised of continuous measurement operations. Moreover, it is unclear where true signal begins and noise ends. Therefore, we have developed an approach to MS data analysis using new types of data visualization and mining operations in which data reduction is accomplished by culling via the intensity of the peaks themselves instead of by location. Applying this new analysis method on a large study set of high resolution mass spectra from healthy and ovarian cancer patients, shows that all of the diagnostic information is contained within the very lowest amplitude regions of the mass spectra. This region can then be selected and studied to identify the exact location and amplitude of the diagnostic biomarkers.

Blood Proteins↗
PubMed2006

Data-mining methods as useful tools for predicting individual drug response: application to CYP2D6 data.

OBJECTIVES: Selecting a maximally informative subset of polymorphisms to predict a clinical outcome, such as drug response, requires appropriate search methods due to the increased dimensionality associated with looking at multiple genotypes. In this study, we investigated the ability of several pattern recognition methods to identify the most informative markers in the CYP2D6 gene for the prediction of CYP2D6 metabolizer status. METHODS: Four data-mining tools were explored: decision trees, random forests, artificial neural networks, and the multifactor dimensionality reduction (MDR) method. Marker selection was performed separately in eight population samples of different ethnic origin to evaluate to what extent the most informative markers differ across ethnic groups. RESULTS: Our results show that the number of polymorphisms required to predict CYP2D6 metabolic phenotype with a high accuracy can be dramatically reduced owing to the strong haplotype block structure observed at CYP2D6. MDR and neural networks provided nearly identical results and performed the best. CONCLUSION: Data-mining methods, such as MDR and neural networks, appear as promising tools to improve the efficiency of genotyping tests in pharmacogenetics with the ultimate goal of pre-screening patients for individual therapy selection with minimum genotyping effort.

Cytochrome P-450 CYP2D6↗
PubMed2003

Data mining: qualitative analysis with health informatics data.

The new computational algorithms emerging in the data mining literature--in particular, the self-organizing map (SOM) and decision tree analysis (DTA)--offer qualitative researchers a unique set of tools for analyzing health informatics data. The uniqueness of these tools is that although they can be used to find meaningful patterns in large, complex quantitative databases, they are qualitative in orientation. To illustrate the utility of these tools, the authors review the two most popular: the SOM and DTA. They provide a basic definition of health informatics, focusing on how data mining assists this field, and apply the SOM and DTA to a hypothetical example to demonstrate what these tools are and how qualitative researchers can use them.

Algorithms↗
PubMed1999

Treatment of complex calcaneal fractures with bony defects from land mine blast injuries with a circular external fixator.

Four calcanei, which were massively destroyed as a result of blasting injuries from land mines, and 18 severely comminuted calcaneal fractures with bony defects (in one patient, both feet), also from land mines, were treated using a circular external fixator for reconstruction by osteogenic distraction. Because of the severe destruction of tissue to be treated, definitive orthopaedic treatment was delayed for 3 to 28 months (average, 10.1 months) after injury. Time from first use of the circular external fixator to removal of the apparatus ranged from 110 to 175 days (average, 143.7 days). Mean follow-up was 18 months. Of the 22 calcaneal injuries, 4 had excellent results, 11 good, 5 fair, and 2 had poor results. Better results were achieved in cases with lesser defects in bone and where definitive orthopaedic treatment was delayed until problems in soft tissue could be treated.

Adult↗
PubMed2002

Outlier mining in high throughput screening experiments.

A data mining procedure for the rapid scoring of high-throughput screening (HTS) compounds is presented. The method is particularly useful for monitoring the quality of HTS data and tracking outliers in automated pharmaceutical or agrochemical screening, thus providing more complete and thorough structure-activity relationship (SAR) information. The method is based on the utilization of the assumed relationship between the structure of the screened compounds and the biological activity on a given screen expressed on a binary scale. By means of a data mining method, a SAR description of the data is developed that assigns probabilities of being a hit to each compound of the screen. Then, an inconsistency score expressing the degree of deviation between the adequacy of the SAR description and the actual biological activity is computed. The inconsistency score enables the identification of potential outliers that can be primed for validation experiments. The approach is particularly useful for detecting false-negative outliers and for identifying SAR-compliant hit/nonhit borderline compounds, both of which are classes of compounds that can contribute substantially to the development and understanding of robust SARs. In a first implementation of the method, one- and two-dimensional descriptors are used for encoding molecular structure information and logistic regression for calculating hits/nonhits probability scores. The approach was validated on three data sets, the first one from a publicly available screening data set and the second and third from in-house HTS screening campaigns. Because of its simplicity, robustness, and accuracy, the procedure is suitable for automation.

Algorithms↗
PubMed2003

Dental data mining: potential pitfalls and practical issues.

Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports (e.g., Kattan et al., 1998) show that ANN and CART models can perform better than classic regression models: CART models excel at covariate interactions, while ANN models excel at nonlinear covariates. Model prediction performance is examined with the use of validation procedures and evaluating concordance, sensitivity, specificity, and likelihood ratio. To aid interpretation, various plots of predicted probabilities are utilized, such as lift charts, receiver operating characteristic curves, and cumulative captured-response plots. A dental caries study is used as an illustrative example. This paper compares the performance of logistic regression with KDD methods of CART and ANN in analyzing data from the Rochester caries study. With careful analysis, such as validation with sufficient sample size and the use of proper competitors, problems of naïve KDD analyses (Schwarzer et al., 2000) can be carefully avoided.

Child↗
PubMed2003

PubMatrix: a tool for multiplex literature mining.

BACKGROUND: Molecular experiments using multiplex strategies such as cDNA microarrays or proteomic approaches generate large datasets requiring biological interpretation. Text based data mining tools have recently been developed to query large biological datasets of this type of data. PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence. RESULTS: For example, a simple term selection procedure allows automatic pair-wise comparisons of approximately 1-100 search terms versus approximately 1-10 modifier terms, resulting in up to 1,000 pair wise comparisons. The matrix table of pair-wise comparisons can then be surveyed, queried individually, and archived. Lists of keywords can include any terms currently capable of being searched in PubMed. In the context of cDNA microarray studies, this may be used for the annotation of gene lists from clusters of genes that are expressed coordinately. An associated PubMatrix public archive provides previous searches using common useful lists of keyword terms. CONCLUSIONS: In this way, lists of terms, such as gene names, or functional assignments can be assigned genetic, biological, or clinical relevance in a rapid flexible systematic fashion. http://pubmatrix.grc.nia.nih.gov/

Cell Line, Tumor↗
PubMed2004

Content-rich biological network constructed by mining PubMed abstracts.

BACKGROUND: The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public. RESULTS: We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. CONCLUSIONS: Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.

Biology↗
PubMed2006

Mining gene expression data by interpreting principal components.

BACKGROUND: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis. RESULTS: We present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that facilitates visualization and subsequent data mining of the independent sources of significant variation present in gene microarray expression datasets (or in any other similarly structured high-dimensional dataset). We applied these tools to two public datasets, and highlight sets of genes most affected by specific subsets of conditions (e.g. tissues, treatments, samples, etc.). Statistically significant associations for highlighted gene sets were shown via global analysis for Gene Ontology term enrichment. Together with covariate associations, the tool provides a basis for building testable hypotheses about the biological or experimental causes of observed variation. CONCLUSION: We provide an unsupervised data mining technique for diverse microarray expression datasets that is distinct from major methods now in routine use. In test uses, this method, based on publicly available gene annotations, appears to identify numerous sets of biologically relevant genes. It has proven especially valuable in instances where there are many diverse conditions (10's to hundreds of different tissues or cell types), a situation in which many clustering and ordering algorithms become problematic. This approach also shows promise in other topic domains such as multi-spectral imaging datasets.

Algorithms↗
PubMed2006

Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line.

BACKGROUND: Sphingosine 1-phosphate (S1P), a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma is not fully understood. Particularly, the concerted role played by matrix metalloproteinases (MMP) and S1P in aggressive tumor behavior and angiogenesis remains to be elucidated. RESULTS: To gain new insights in the effect of S1P on angiogenesis and invasion of this type of malignant tumor, we used microarrays to investigate the gene expression in glioblastoma as a response to S1P administration in vitro. We compared the expression profiles for the same cell lines under the influence of epidermal growth factor (EGF), an important growth factor. We found a set of 72 genes that are significantly differentially expressed as a unique response to S1P. Based on the result of mining full-text articles from 20 scientific journals in the field of cancer research published over a period of five years, we inferred gene-gene interaction networks for these 72 differentially expressed genes. Among the generated networks, we identified a particularly interesting one. It describes a cascading event, triggered by S1P, leading to the transactivation of MMP-9 via neuregulin-1 (NRG-1), vascular endothelial growth factor (VEGF), and the urokinase-type plasminogen activator (uPA). This interaction network has the potential to shed new light on our understanding of the role played by MMP-9 in invasive glioblastomas. CONCLUSION: Automated extraction of information from biological literature promises to play an increasingly important role in biological knowledge discovery. This is particularly true for high-throughput approaches, such as microarrays, and for combining and integrating data from different sources. Text mining may hold the key to unraveling previously unknown relationships between biological entities and could develop into an indispensable instrument in the process of formulating novel and potentially promising hypotheses.

Cell Line, Tumor↗