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PubMed1997

Automated detection of hereditary syndromes using data mining.

Computer-based data mining methodology applied to family history clinical data can algorithmically create highly accurate, clinically oriented hereditary disease pattern recognizers. For the example of hereditary colon cancer, the data mining's selection of relevant factors to assess for hereditary colon cancer was statistically significant (P < 0.05). All final recognizer-formulated patterns of hereditary colon cancer were independently confirmed by a clinical expert. Applied to previously analyzed family histories, the recognizer identified the definitive hereditary histories, correctly responded negatively to the putative hereditary histories, and correctly responded negatively to empirically elevated colon cancer risk situations. This capability facilitates patient selection for DNA studies in search of gene mutations. When genetic mutations are included as parameters in a patient database for a genetic disease, the process yields an expert system which characterizes variations in clinical disease presentations in terms of genetic mutations. Such information can greatly improve the efficiency of gene testing.

Adult↗
PubMed2001

Whole-body gene expression by data mining.

To date, a comprehensive survey of the expression of lysyl oxidase (LOX), lysyl oxidase-like 1 (LOXL1), and lysyl oxidase-like 2 (LOXL2) has yet to be performed. The use of in vitro strategies to accomplish this task would prove daunting as it is both time-consuming and costly. We present a new in silico data mining strategy that directly addresses these limitations. Sequences corresponding to the 3' untranslated regions of LOX, LOXL1, and LOXL2 were individually queried against the human expressed sequence tag database (dbEST). In this manner, the entire tissue repertoire available in the dbEST was surveyed. This provided an estimate of the levels of mRNA transcripts in a variety of adult and fetal tissues. We have also employed this strategy to determine the pattern of expression and levels of a newly discovered gene, CGI-15. The veracity of this technique has been independently assessed by semiquantitative PCR analysis. The application of this technology is bounded only by the ever-growing information available in the GenBank, UniGene, and human EST databases. The utility of our data mining strategy to establish relative transcript levels in numerous tissues is presented.

3' Untranslated Regions↗
PubMedDate not supplied

A data mining approach for analyzing density maps representing macromolecular structures.

Results of electron microscopy-based three-dimensional reconstructions of macromolecules or their complexes are usually stored as density maps. Each point ("voxel") in the map represents a density value and one approach for studying details of the map is to display an isosurface enclosing areas of interest. We have taken a data mining approach not only focusing on the areas of immediate interest but determining all possible separate entities ("blobs") from a density map. After the entire density map is analyzed with our mining program BLOBBER, properties of all detected blobs can be browsed and sets of blobs can be visualized using our VIZBLOB program. Since BLOBBER analyzes density maps using only density information and relates it to spatial relationships, BLOBBER can be used to analyze symmetrical or asymmetrical density maps from any source. To test our program we have analyzed published bacteriophage PRD1 reconstructions. We identified various structural details ranging from individual proteins to major complexes such as the whole capsid shell and more elaborate details of possible connections between membrane interfaces. This approach can also be a useful preprocessing tool for visualizing reconstructions.

Bacteriophages↗
PubMedDate not supplied

TransMiner: mining transitive associations among biological objects from text.

Associations among biological objects such as genes, proteins, and drugs can be discovered automatically from the scientific literature. TransMiner is a system for finding associations among objects by mining the Medline database of the scientific literature. The direct associations among the objects are discovered based on the principle of co-occurrence in the form of an association graph. The principle of transitive closure is applied to the association graph to find potential transitive associations. The potential transitive associations that are indeed direct are discovered by iterative retrieval and mining of the Medline documents. Those associations that are not found explicitly in the entire Medline database are transitive associations and are the candidates for hypothesis generation. The transitive associations were ranked based on the sum of weight of terms that co-occur with both the objects. The direct and transitive associations are visualized using a graph visualization applet. TransMiner was tested by finding associations among 56 breast cancer genes and among 24 objects in the calpain signal transduction pathway. TransMiner was also used to rediscover associations between magnesium and migraine.

Abstracting and Indexing↗
PubMed2006

Preservation procedures for arsenic speciation in a stream affected by acid mine drainage in southwestern Spain.

A preservation study has been performed for arsenic speciation in surface freshwaters affected by acid mine drainage (AMD), a pollution source characterized by low pH and high metallic content. Two sample preservation procedures described in the literature were attempted using opaque glass containers and refrigeration: i) addition of 0.25 mol L(-1) EDTA to the samples, which maintained the stability of the arsenic species for 3 h; and ii) in situ sample clean-up with a cationic exchange resin, in order to reduce the metallic load, which resulted in a partial co-adsorption of arsenic onto Fe precipitates. A new proposed method was also tried: sample acidification with 6 mol L(-1) HCl followed by in situ clean-up with a cationic exchange resin, which allowed a longer preservation time of at least 48 h. The proposed method was successfully applied to water samples with high arsenic content, taken from the Aguas Agrias Stream (Odiel River Basin, SW Spain), which is severely affected by AMD that originates at the nearby polymetallic sulfide mine of Tharsis. The speciation results obtained by liquid chromatography-hydride generation-atomic fluorescence spectrometry (HPLC-HG-AFS) indicated that during the summer the main arsenic species was As(V) at the hundred microg L(-1) level, followed by DMA (dimethyl arsenic) and As(III) below the ten microg L(-1) level. In winter, As(V) and As(III) increased at least fivefold, whereas the DMA was not detected.

Journal Article↗
PubMed2005

Bioaccumulation and toxicity of dissolved heavy metals from the Guadalquivir Estuary after the Aznalcóllar mining spill using Ruditapes philippinarum.

The shore clam Ruditapes philippinarum was used as a biomonitoring organism to measure the potential impact that the mining spill in the Guadalquivir Estuary (SW, Spain) in 1998 may have exerted on local biota. Individuals were exposed to dissolved cadmium, copper, and zinc at concentrations found in local waters after the spill (3 microg x L(-1) Cd, 15 microg x L(-1) Cu, 700 microg x L(-1) Zn) at two salinity values: 10 and 35. Residues of metals were measured in gill and digestive gland, together with metallothioneins in the digestive gland and histopathological lesions in gill, digestive gland, and gonad tissues over time. Heavy metals Zn and Cd associated with the mining spill, were bioaccumulated in clam tissues, associated with the activation of metallothioneins, and related to the histopathological lesions measured at all the clam tissues. The heavy metal Cu not related to the spill was not directly associated with effects measured. The bioaccumulation and adverse effects associated with Cd and Zn were significantly higher at low salinity (10) than at high salinity (35) values.

Animals↗
PubMed2001

Design and Performance of rRNA Targeted Oligonucleotide Probes for in Situ Detection and Phylogenetic Identification of Microorganisms Inhabiting Acid Mine Drainage Environments.

At Iron Mountain, CA, there is an extreme occurrence of acid mine drainage (AMD). This is a result of past mining activity that has exposed a sulfide ore body to weathering and microbial activity. This study presents seven new oligonucleotide probes for the detection of microorganisms at this AMD site by fluorescent in situ hybridization. In the design of these probes we have accounted for a large body of 16S rRNA sequence data recently compiled by us. This was obtained by PCR and cloning directly from environmental DNA and was mostly represented by novel sequences. The probes were developed to include detection of novel and uncultivated organisms. This includes detection for the Thermoplasmales group, a new group of Leptospirillum, the genus Sulfobacillus, the Acidiphilium genus, Acidimicrobium and relatives, and for organisms within the delta Proteobacteria. These probes have been used to examine the abundance and distribution of organisms, including novel and uncultivated taxa, and to clarify their potential contributions to AMD production at the site. We anticipate that these probes will be useful tools for exploration of the microbiology of other natural acidic environments and bioleaching systems.

Journal Article↗
PubMed2005

Bacterial community shift along a subsurface geothermal water stream in a Japanese gold mine.

Change of bacterial community occurring along a hot water stream in the Hishikari gold mine, Japan, was investigated by applying a combination of various culture-independent techniques. The stream, which is derived from a subsurface anaerobic aquifer containing plentiful CO2, CH4, H2, and NH4+, emerges in a mine tunnel 320 m below the surface providing nutrients for a lush microbial community that extends to a distance of approximately 7 m in the absence of sunlight-irradiation. Over this distance, the temperature decreases from 69 degrees C to 55 degrees C, and the oxidation-reduction potential increases from -130 mV to +59 mV. In the hot upper reaches of the stream, the dominant phylotypes were: 1) a deeply branching lineage of thermophilic methane-oxidizing gamma-Proteobacteria, and 2) a thermophilic hydrogen- and sulfur-oxidizing Sulfurihydrogenibium sp. In contrast, the prevailing phylotypes in the middle and lower parts of the stream were closely related to ammonia-oxidizing Nitrosomonas and nitrite-oxidizing Nitrospira spp.. Changes in the microbial metabolic potential estimated by competitive PCR analysis of genes encoding the enzymes, particulate methane monooxygenase (pmoA), ammonia monooxygenase (amoA), and putative nitrite oxidoreductase (norB), also substantiated the community shift indicated by 16S rRNA gene analysis. The diversity of putative norB lineages was assessed for the first time in the hot water environment. Estimation of dominant phylotypes by whole-cell fluorescent in situ hybridization and changes in inorganic nitrogen compounds such as decreasing ammonium and increasing nitrite and nitrate in the mat-interstitial water along the stream were consistent with the observed transition of the bacterial community structure in the stream.

Ammonia↗
PubMed2005

Development of EST-SSR markers by data mining in three species of shrimp: Litopenaeus vannamei, Litopenaeus stylirostris, and Trachypenaeus birdy.

We report on the data mining of publicly available Litopenaeus vannamei expressed sequence tags (ESTs) to generate simple sequence repeat (SSRs) markers and on their transferability between related Penaeid shrimp species. Repeat motifs were found in 3.8% of the evaluated ESTs at a frequency of one repeat every 7.8 kb of sequence data. A total of 206 primer pairs were designed, and 112 loci were amplified with the highest success in L. vannamei. A high percentage (69%) of EST-SSRs were transferable within the genus Litopenaeus. More than half of the amplified products were polymorphic in a small testing panel of L. vannamei. Evaluation of those primers in a larger testing panel showed that 72% of the markers fit Hardy-Weinberg equilibrium, which shows their utility for population genetic analysis. Additionally, a set of 26 of the EST-SSRs were evaluated for Mendelian segregation. A high percentage of monomorphic markers (46%) proved to be polymorphic by singles-stranded conformational polymorphism analysis. Because of the high number of ESTs available in public databases, a data mining approach similar to the one outlined here might yield high numbers of SSR markers in many animal taxa.

Animals↗
PubMed2006

Volatile emissions from Aesculus hippocastanum induced by mining of larval stages of Cameraria ohridella influence oviposition by conspecific females.

Larval stages of the horse chestnut leafminer Cameraria ohridella can completely destroy the surface of horse chestnut leaves, Aesculus hippocastanum. This study investigated the effect of the degree of leaf browning caused by the insect's larvae on olfactory detection, aggregation, and oviposition of C. ohridella adults. The influence of A. hippocastanum flower scent on oviposition of the first generation was also evaluated. Utilizing gas chromatography coupled with parallel detection by mass spectrometry and electroantennography (GC-MS/EAD), more than 30 compounds eliciting responses from antennae of C. ohridella were detected. Oviposition and mining by C. ohridella caused significant changes in the profile of leaf volatiles of A. hippocastanum. After oviposition and subsequent mining by early larval stages (L1-L3), substances such as benzaldehyde, 1,8-cineole, benzyl alcohol, 2-phenylethanol, methyl salicylate, (E)-beta-caryophyllene, and (E,E)-alpha-farnesene were emitted in addition to the compounds emitted by uninfested leaves. Insects were able to detect these compounds. The emitted amount of these substances increased with progressive larval development. During late larval stages (L4, L5) and severe loss of green leaf area, (E,E)-2,4-hexadienal, (E/Z)-linalool oxide (furanoid), nonanal, and decanal were also released by leaves. These alterations of the profile of volatiles caused modifications in aggregation of C. ohridella on leaves. In choice tests, leaves in early infestation stages showed no significant effect on aggregation, whereas insects avoided leaves in late infestation stages. Further choice tests with leaves treated with single compounds led to the identification of substances mediating an increase or decrease in oviposition.

Aesculus↗
PubMed2006

A comparative study of cells in inflammation, EAE and MS using biomedical literature data mining.

Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average gamma = 2.1) that are commonly found in biological networks.

Animals↗
PubMed2006

Prediction of blood glucose level of type 1 diabetics using response surface methodology and data mining.

In order to improve the accuracy of predicting blood glucose levels, it is necessary to obtain details about the lifestyle and to optimize the input variables dependent on diabetics. In this study, using four subjects who are type 1 diabetics, the fasting blood glucose level (FBG), metabolic rate, food intake, and physical condition are recorded for more than 5 months as a preliminary study. Then, using data mining, an estimation model of FBG is obtained, and subsequently, the trend in fluctuations in the next morning's glucose level is predicted. The subject's physical condition is self-assessed on a scale from positive (1) to negative (5), and the values are set as the physical condition variable. By adding the physical condition variable to the input variables for the data mining, the accuracy of the FBG prediction is improved. In order to determine more appropriate input variables from the biological information reflecting on the subject's glucose metabolism, response surface methodology (RSM) is employed. As a result, using the variables exhibiting positive correlations with the FBG in the RSM, the accuracy of the FBG prediction improved. Conditions could be found such that the accuracy of the predicting trends in fluctuations in blood glucose level reached around 80%. The prediction method of the trend in fluctuations in the next morning's glucose levels might be useful to improve the quality of life of type 1 diabetics through insulin treatment, and to prevent hypoglycemia.

Adult↗
PubMed1989

Impact of industrial and mine drainage wastes on the heavy metal distribution in the drainage basin and estuary of the Sado River (Portugal).

This paper presents results from a survey of the heavy metal distribution in sediments in the drainage basin and estuary of the Sado River (Portugal). In the drainage basin, heavy metals originate mostly from pyrite outcrop erosion and mining activities (Cd, Zn, Cu and locally Hg, Pg), and also from crust erosion (Sn, Ni, Ti, Zr). These sources are not correlated with the particulate organic carbon (POC) and so the metals are thought to be in inorganic forms in this area. Anthropogenic heavy metal sources (urban and industrial) are found in the lower estuary (Sn, Cd, Hg, Zn, Pb and Cu) along with high POC concentrations. In this zone, these metals are thought to be strongly adsorbed onto organic particles. Furthermore, organo-metallic species are likely to be present, as demonstrated in the case of Sn, since methyl- and butyl-tin species were detected in sediments from this area. This suggests the need for the detection of organo-metallic species to understand the heavy metal geochemical cycles. No long-term changes in metal concentrations are found in sediment cores, except in the middle estuary (Zn, Cu) due to the development of mining activities on an industrial scale in the 1860s.

Journal Article↗
PubMed1994

Effects of mycorrhizae and other soil microbes on revegetation of heavy metal contaminated mine spoil.

The effects of mycorrhizal fungi and other soil microorganisms on growth of two grasses, Andropogon gerardii Vitm. and Festuca arundinacea Schreb., in heavy metal-contaminated soil and mine tailings were investigated. A. gerardii is highly dependent on mycorrhizal fungi in native prairie, while F. arundinacea is a facultative mycotroph and relies on mycorrhizal symbiosis only in extremely infertile soils. Regardless of microbial amendments, neither plant species was able to establish and grow in the mine tailings. Both plant species grew in the moderately contaminated or non-contaminated soils, although A. gerardii grew in these soils only when mycorrhizal. Other soil microbes significantly improved growth of A. gerardii only in uncontaminated soil, but to a lesser extent than mycorrhizae. Although F. arundinacea was more highly colonized by mycorrhizal fungi than A. gerardii, neither microbial amendment affected growth of fescue in any soil. In several treatments mycorrhizal fungi adapted to uncontaminated soil stimulated plant growth more than mycorrhizae adapted to the moderately contaminated soil. However, mycorrhizal fungi adapted to contaminated soil did not increase the productivity of plant growth in contaminated soil more than fungi adapted to uncontaminated soil. A. gerardii plants inoculated with mycorrhizal fungi retained more Zn in roots than in shoots, confirming earlier reports that mycorrhizal fungi alter the translocation pattern of heavy metals in host plants. In contrast, mycorrhizae did not affect translocation patterns in F. arundinaceae, suggesting that the mycorrhizal dependence of a plant species is correlated with the retention of metals in roots. The correlation between mycorrhizal dependence of a plant species and mycorrhizal alteration of translocation pattern may also explain the inconsistent reports of mycorrhizal effects on translocation of heavy metals in plants. Plant response to mycorrhizal symbiosis may therefore provide a useful criterion for the selection of the plant species to be used in revegetation of contaminated sites.

Journal Article↗
PubMed2004

Data mining of gene expression changes in Alzheimer brain.

Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons. Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.

Alzheimer Disease↗
PubMed2005

Predicting breast cancer survivability: a comparison of three data mining methods.

OBJECTIVE: The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Therefore, the main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. METHODS AND MATERIAL: We used two popular data mining algorithms (artificial neural networks and decision trees) along with a most commonly used statistical method (logistic regression) to develop the prediction models using a large dataset (more than 200,000 cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. RESULTS: The results indicated that the decision tree (C5) is the best predictor with 93.6% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), artificial neural networks came out to be the second with 91.2% accuracy and the logistic regression models came out to be the worst of the three with 89.2% accuracy. CONCLUSION: The comparative study of multiple prediction models for breast cancer survivability using a large dataset along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data mining methods. Using sensitivity analysis on neural network models provided us with the prioritized importance of the prognostic factors used in the study.

Breast Neoplasms↗
PubMed2005

Temporal data mining for the quality assessment of hemodialysis services.

OBJECTIVE: This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. METHODS: Intelligent data analysis and temporal data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several pre-processing techniques, comprising data reduction, multi-scale filtering and temporal abstractions. RESULTS: We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the outcome parameters and the measured variables were examined by the domain experts, which were able to distinguish between rules confirming available background knowledge and unexpected but plausible rules. CONCLUSION: The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients' behavior.

Algorithms↗
PubMed2006

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

OBJECTIVE: The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. MATERIALS AND METHODS: This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. RESULTS: We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. CONCLUSION: We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

Animals↗