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At least 1,279 records · Page 71Linked to original sources
PubMed2006

Mining frequent patterns for AMP-activated protein kinase regulation on skeletal muscle.

BACKGROUND: AMP-activated protein kinase (AMPK) has emerged as a significant signaling intermediary that regulates metabolisms in response to energy demand and supply. An investigation into the degree of activation and deactivation of AMPK subunits under exercise can provide valuable data for understanding AMPK. In particular, the effect of AMPK on muscle cellular energy status makes this protein a promising pharmacological target for disease treatment. As more AMPK regulation data are accumulated, data mining techniques can play an important role in identifying frequent patterns in the data. Association rule mining, which is commonly used in market basket analysis, can be applied to AMPK regulation. RESULTS: This paper proposes a framework that can identify the potential correlation, either between the state of isoforms of alpha, beta and gamma subunits of AMPK, or between stimulus factors and the state of isoforms. Our approach is to apply item constraints in the closed interpretation to the itemset generation so that a threshold is specified in terms of the amount of results, rather than a fixed threshold value for all itemsets of all sizes. The derived rules from experiments are roughly analyzed. It is found that most of the extracted association rules have biological meaning and some of them were previously unknown. They indicate direction for further research. CONCLUSION: Our findings indicate that AMPK has a great impact on most metabolic actions that are related to energy demand and supply. Those actions are adjusted via its subunit isoforms under specific physical training. Thus, there are strong co-relationships between AMPK subunit isoforms and exercises. Furthermore, the subunit isoforms are correlated with each other in some cases. The methods developed here could be used when predicting these essential relationships and enable an understanding of the functions and metabolic pathways regarding AMPK.

AMP-Activated Protein Kinases↗
PubMed2006

A Fourier transformation based method to mine peptide space for antimicrobial activity.

BACKGROUND: Naturally occurring antimicrobial peptides are currently being explored as potential candidate peptide drugs. Since antimicrobial peptides are part of the innate immune system of every living organism, it is possible to discover new candidate peptides using the available genomic and proteomic data. High throughput computational techniques could also be used to virtually scan the entire peptide space for discovering out new candidate antimicrobial peptides. RESULT: We have identified a unique indexing method based on biologically distinct characteristic features of known antimicrobial peptides. Analysis of the entries in the antimicrobial peptide databases, based on our indexing method, using Fourier transformation technique revealed a distinct peak in their power spectrum. We have developed a method to mine the genomic and proteomic data, for the presence of peptides with potential antimicrobial activity, by looking for this distinct peak. We also used the Euclidean metric to rank the potential antimicrobial peptides activity. We have parallelized our method so that virtually any given protein space could be data mined, in search of antimicrobial peptides. CONCLUSION: The results show that the Fourier transform based method with the property based coding strategy could be used to scan the peptide space for discovering new potential antimicrobial peptides.

Amino Acid Sequence↗
PubMed2006

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.

Animals↗
PubMed2005

Mining for diagnostic information in body surface potential maps: a comparison of feature selection techniques.

BACKGROUND: In body surface potential mapping, increased spatial sampling is used to allow more accurate detection of a cardiac abnormality. Although diagnostically superior to more conventional electrocardiographic techniques, the perceived complexity of the Body Surface Potential Map (BSPM) acquisition process has prohibited its acceptance in clinical practice. For this reason there is an interest in striking a compromise between the minimum number of electrocardiographic recording sites required to sample the maximum electrocardiographic information. METHODS: In the current study, several techniques widely used in the domains of data mining and knowledge discovery have been employed to mine for diagnostic information in 192 lead BSPMs. In particular, the Single Variable Classifier (SVC) based filter and Sequential Forward Selection (SFS) based wrapper approaches to feature selection have been implemented and evaluated. Using a set of recordings from 116 subjects, the diagnostic ability of subsets of 3, 6, 9, 12, 24 and 32 electrocardiographic recording sites have been evaluated based on their ability to correctly asses the presence or absence of Myocardial Infarction (MI). RESULTS: It was observed that the wrapper approach, using sequential forward selection and a 5 nearest neighbour classifier, was capable of choosing a set of 24 recording sites that could correctly classify 82.8% of BSPMs. Although the filter method performed slightly less favourably, the performance was comparable with a classification accuracy of 79.3%. In addition, experiments were conducted to show how (a) features chosen using the wrapper approach were specific to the classifier used in the selection model, and (b) lead subsets chosen were not necessarily unique. CONCLUSION: It was concluded that both the filter and wrapper approaches adopted were suitable for guiding the choice of recording sites useful for determining the presence of MI. It should be noted however that in this study recording sites have been suggested on their ability to detect disease and such sites may not be optimal for estimating body surface potential distributions.

Algorithms↗
PubMed2006

A survey of data mining methods for linkage disequilibrium mapping.

Data mining methods are gaining more interest as potential tools in mapping and identification of complex disease loci. The methods are well suited to large numbers of genetic marker loci produced by high-throughput laboratory analyses, but also might be useful for clarifying the phenotype definitions prior to more traditional mapping analyses. Here, the current data mining-based methods for linkage disequilibrium mapping and phenotype analyses are reviewed.

Chromosome Mapping↗
PubMed2005

Text-mining and information-retrieval services for molecular biology.

Text-mining in molecular biology -- defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents -- has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators.

Computational Biology↗
PubMed2005

Pharmacogenomic responses of rat liver to methylprednisolone: an approach to mining a rich microarray time series.

A data set was generated to examine global changes in gene expression in rat liver over time in response to a single bolus dose of methylprednisolone. Four control animals and 43 drug-treated animals were humanely killed at 16 different time points following drug administration. Total RNA preparations from the livers of these animals were hybridized to 47 individual Affymetrix RU34A gene chips, generating data for 8799 different probe sets for each chip. Data mining techniques that are applicable to gene array time series data sets in order to identify drug-regulated changes in gene expression were applied to this data set. A series of 4 sequentially applied filters were developed that were designed to eliminate probe sets that were not expressed in the tissue, were not regulated by the drug treatment, or did not meet defined quality control standards. These filters eliminated 7287 probe sets of the 8799 total (82%) from further consideration. Application of judiciously chosen filters is an effective tool for data mining of time series data sets. The remaining data can then be further analyzed by clustering and mathematical modeling techniques.

Adrenalectomy↗
PubMed1992

Decreased sialidase activity in alveolar macrophages of guinea pigs exposed to coal mine dust.

The origin of immune dysfunctions that are observed in pneumoconiotic miners still remains unknown. There is evidence that the carbohydrate moiety of membrane glycoconjugates is of primary importance in many functions of immunocompetent cells. The glycosylation, and especially the sialylation level of membrane components of various lymphocyte and macrophage subsets, vary depending on the state of cellular differentiation and activation. Sialidases, which may regulate the amount of sialic acids exposed on the cell membrane, can thus be considered as immunoregulatory enzymes. In this report, the sialidase activity has been measured in alveolar macrophages (AM) and in cell-free bronchoalveolar lavage fluid (BALF) from guinea pigs exposed for 4 months to coal mine dust at a concentration of 300 mg/m3. The samples were collected by bronchoalveolar lavage 2 months after cessation of exposure. The sialidase activity in the cell-free fluid and in the purified alveolar macrophages showed a 10-fold decrease (p less than 0.001). Kinetic parameters of the enzyme such as Km and optimum pH did not change. This changed activity was specific for sialidase, as two other lysosomal glycosidases, beta-galactosidase and N-acetylglucosaminidase, showed unchanged activities. These results suggest the possibility that, by inducing a decreased sialidase activity, exposure to coal mine dust may lead to a modified expression of AM membrane-associated sialic acids giving rise to altered immune functions (i. e., phagocytosis, antigen processing, response to cytokines, etc.).

Animals↗
PubMed2006

Fiber-optic laser sensor for mine detection and verification.

What we believe to be a new optical approach for the identification of mines and explosives by analyzing the surface materials and not only bulk is developed. A conventional manually operated mine prodder is upgraded by laser-induced breakdown spectroscopy (LIBS). In situ and real-time information of materials that are in front of the prodder are obtained during the demining process in order to optimize the security aspects and the speed of demining. A Cr4+:Nd3+:YAG microchip laser is used as a seed laser for an ytterbium-fiber amplifier to generate high-power laser pulses at 1064 nm with pulse powers up to E(p) = 1 mJ, a repetition rate of f(rep.) = 2-20 kHz and a pulse duration of t(p) = 620 ps. The recorded LIBS signals are analyzed by applying neural networks for the data analysis.

Journal Article↗
PubMed2001

Monte Carlo simulations of electromagnetic wave scattering from a random rough surface with three-dimensional penetrable buried object: mine detection application using the steepest-descent fast multipole method.

We present a statistical study of the electric field scattered from a three-dimensional penetrable object buried under a two-dimensional random rough surface. Monte Carlo simulations using the steepest-descent fast multipole method (SDFMM) are conducted to calculate the average and the standard deviation of the near-zone scattered fields. The SDFMM, originally developed at the University of Illinois at Urbana-Champaign, has been modified to calculate the unknown surface currents both on the rough ground and on the buried object that are due to excitation by a tapered Gaussian beam. The rough ground medium used is an experimentally measured typical dry Bosnian soil with 3.8% moisture, while the buried object represents a plastic land mine modeled as an oblate spheroid with dimensions and burial depth smaller than the free-space wavelength. Both vertical and horizontal polarizations for the incident waves are studied. The numerical results show that the TNT mine signature is almost 5% of the total field scattered from the ground. Moreover, relatively recognizable object signatures are observed even when the object is buried under the tail of the incident beam. Interestingly, even for the small surface roughness parameters considered, the standard deviation of the object signature is almost 30% of the signal itself, indicating significant clutter distortion that is due to the roughness of the ground.

Journal Article↗
PubMed2005

Systematic association of genes to phenotypes by genome and literature mining.

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene-phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.

Bacteria↗
PubMed2001

Application of data mining for examining polypharmacy and adverse effects in cardiology patients.

This article comments upon the use of data mining tools to examine clinical data. Many cardiovascular patients have co-morbid diseases that put them at risk for polypharmacy, or severe adverse reactions from the interactions of multiple medications. Clinical trials typically use too few patients with stringent inclusion/exclusion criteria that prevent an examination of the issue of polypharmacy. However, clinical data collected in the course of patient treatment can be used in conjunction with data mining to find meaningful results.

Clinical Trials as Topic↗
PubMed2005

Geochemical modeling of gold precipitation conditions in the Bloco do Butiá Mine, Lavras do Sul/Brazil.

A geochemical modeling of gold deposition was performed using the EQ3/EQ6 software package using conditions inferred from geological, petrographic, geochemical and fluid inclusion data from the Bloco do Butiá gold mine, Lavras do Sul, RS. Gold in the mine occurs only in the pyrite structure (invisible gold). The pyrite occurs associated with white mica (phengite) in the zone of phyllic alteration. The process of gold deposition showed to be related to temperature and pH decrease. The pH decrease was fundamental to gold deposition by destabilization of sulfur species [Au(HS)2- , HAu(HS)2(0) and Au(HS)0] dissolved in the aqueous solution, being Au(HS)0 the main gold transporting complex. The addition of KCl is hard to accept as cause of gold precipitation because no Cl- was detected in phengite. However, the geochemical mass balance calculation resulted in the gain of some potassium in the zone of phyllic alteration. The precipitation of pyrite (+/- auriferous) may have been strongly influenced by iron availability resulting from dissolution of ferrous chlorites by the fluids responsible for phengite deposition. The low salinity in quartz grain fluid inclusions from the propylitized wall rock also indicates the little importance of chlorine as gold transporting agent. Sulfur, and not chlorine, compounds must have dominated the gold transporting complexes in the Bloco do Butiá gold area.

Journal Article↗
PubMed2002

Oviposition on and mining in bolls of Bt and non-Bt cotton by resistant and susceptible pink bollworm (Lepidoptera: Gelechiidae).

Transgenic cotton that produces insecticidal crystal protein Cry1Ac of Bacillus thuringiensis (Bt) has been effective in controlling pink bollworm, Pectinophora gossypiella (Saunders). We compared responses to bolls of Bt cotton and non-Bt cotton by adult females and neonates from susceptible and Cry1Ac-resistant strains of pink bollworm. In choice tests on caged cotton plants in the greenhouse, neither susceptible nor resistant females laid fewer eggs on Bt cotton bolls than on non-Bt cotton bolls, indicating that the Bt toxin did not deter oviposition. Multiple regression revealed that the number of eggs laid per boll was negatively associated with boll age and positively associated with boll diameter. Females also laid more eggs per boll on plants with more bolls. The distribution of eggs among bolls of Bt cotton and non-Bt cotton was clumped, indicating that boll quality rather than avoidance of previously laid eggs was a primary factor in oviposition preference. Parallel to the results from oviposition experiments, in laboratory no-choice tests with 10 neonates per boll, the number of entrance holes per boll did not differ between Bt cotton and non-Bt cotton for susceptible and resistant neonates. Also, like females, neonates preferred younger bolls and larger bolls. Thus, acceptance of bolls by females for oviposition and by neonates for mining was affected by boll age and diameter, but not by Bt toxin in bolls. The lack of discrimination between Bt and non-Bt cotton bolls by pink bollworm from susceptible and resistant strains indicates that oviposition and mining initiation are independent of susceptibility to Cry1Ac.

Animals↗
PubMed2003

Effects of kaolin particle film on oviposition, larval mining, and infestation of cotton by pink bollworm (Lepidoptera: Gelechiidae).

We tested effects of kaolin particle film on oviposition, larval mining, and infestation of cotton by pink bollworm, Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae), in the laboratory, greenhouse, and field. In laboratory choice tests, females laid seven times more eggs on untreated bolls than on bolls treated with kaolin. When neonates were put on bolls in the laboratory, each boll with a treated and untreated half, larvae and mines were found 24 h later on the untreated half but not on the treated half. In oviposition choice tests with whole plants in the greenhouse, females laid four times more eggs on untreated plants than on treated plants and the number of eggs on bolls was five times higher for untreated plants than for treated plants. Kaolin treatments altered the distribution of eggs among plant parts, with untreated bolls receiving a higher percentage than treated bolls, whereas the opposite occurred for petioles. In field tests, treatment with kaolin alone reduced the proportion of bolls infested with pink bollworm, but a mixture of kaolin and the pyrethroid lambda-cyhalothrin was most effective. The results suggest that kaolin particle film may be useful against pink bollworm, particularly in conjunction with other control tactics.

Agriculture↗
PubMed2006

Agricultural soils spiked with copper mine wastes and copper concentrate: implications for copper bioavailability and bioaccumulation.

A better understanding of exposure to and effects of copper-rich pollutants in soils is required for accurate environmental risk assessment of copper. A greenhouse experiment was conducted to study copper bioavailability and bioaccumulation in agricultural soils spiked with different types of copper-rich mine solid wastes (copper ore, tailing sand, smelter dust, and smelter slag) and copper concentrate. A copper salt (copper sulfate, CuSO4) that frequently is used to assess soil copper bioavailability and phytotoxicity also was included for comparison. Results showed that smelter dust, tailing sand, and CuSO4 are more likely to be bioavailable and, thus, toxic to plants compared with smelter slag, concentrate, and ore at equivalent total copper concentrations. Differences may be explained by intrinsic differences in copper solubilization from the source materials, but also by their capability to decrease soil pH (confounding effect). The copper toxicity and bioaccumulation in plants also varied according to soil physicochemical characteristics (e.g., pH and total organic carbon) and the available levels of plant nutrients, such as nitrogen, phosphorus, and potassium. Chemistry/mineralogy of mine materials, soil/pore-water chemistry, and plant physiological status thus should be integrated for building adequate models to predict phytotoxicity and environmental risk of copper.

Agriculture↗
PubMed2007

Vertical distribution and speciation of trace metals in weathering flotation residues of a zinc/lead sulfide mine.

Sulfide-bearing mine tailings are a serious environmental problem around the world. In this study, the vertical distribution and speciation of Zn and Pb in the fine-grained flotation residues of a former sulfide ore mine in Germany were investigated to assess the inorganic weathering processes that effect the environmental risk arising from this site. Total metal contents were determined by X-ray fluorescence spectroscopy (XRF). Mobilizable fractions of Zn, Pb, Fe, and Mn were quantified by sequential chemical extractions (SCE). Furthermore, the speciation of Zn was analyzed by Zn K-edge extended X-ray absorption fine structure spectroscopy (EXAFS) to identify the residual Zn species. The variations in pH and inorganic C content show an acidification of the topsoil to pH 5.5. EXAFS results confirm that Zn is mainly bound in sphalerite in the subsoil and weathering reactions lead to a redistribution of Zn in the topsoil. A loss of 35% Zn and S from the topsoil compared with the parent material with 10 g kg-1 Zn and neutral pH has been observed. If acidification proceeds it will lead to a significant release of Zn, S, and Pb to the ground water. In contrast to Zn, Pb is enriched in the mobile fraction of the topsoil by more than a factor of two compared with the subsoil which contains a total of 2 g kg-1 Pb. Thus, the high bioavailability of Pb and the potential for Pb uptake by plants and animals currently represent the most severe threat for environmental health.

Hydrogen-Ion Concentration↗
PubMed2004

Biomedical literature mining: challenges and solutions in the 'omics' era.

It is now obvious that the rate-limiting step in high throughput experimentation is neither data acquisition nor analysis, but rather our ability to interpret data on a genome-wide scale. Indeed, the explosion of data sampling capacity combined with increasing publication rates greatly impairs our ability to find meaning in vast collections of data. In order to support data interpretation, bioinformatic tools are needed to identify critical information contained in large bodies of literature. However, extracting knowledge embedded in free text is an arduous task, compounded in the biomedical field by an inconsistent gene nomenclature, domain-specific language and restricted access to full text articles. This paper presents a selection of currently available biomedical literature mining software. These tools rely on statistic and, more recently, semantic analyses (Natural Language Processing) to automatically extract information from the literature. In addition, a literature mining strategy has been developed to explore patterns of term occurrences in abstracts. This method automatically identifies relevant keywords in collections of abstracts, and uses a pattern discovery algorithm to generate a visual interface for exploring functional associations among genes. Term occurrence heatmaps can also be combined with gene expression profiles to provide valuable functional annotations. Furthermore, as demonstrated with tumor cell line literature profiling results, this approach can be applied to a variety of themes beyond genomic data analysis. Altogether, these examples illustrate how literature analysis can be employed to support knowledge discovery in biomedical research.

Algorithms↗