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PubMed2003

The University of Minnesota Biocatalysis/Biodegradation Database: post-genomic data mining.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.ahc.umn.edu/) provides curated information on microbial catabolism and related biotransformations, primarily for environmental pollutants. Currently, it contains information on over 130 metabolic pathways, 800 reactions, 750 compounds and 500 enzymes. In the past two years, it has increased its breath to include more examples of microbial metabolism of metals and metalloids; and expanded the types of information it includes to contain microbial biotransformations of, and binding interactions with many chemical elements. It has also increased the ways in which this data can be accessed (mined). Structure-based searching was added, for exact matches, similarity, or substructures. Analysis of UM-BBD reactions has lead to a prototype, guided, pathway prediction system. Guided prediction means that the user is shown all possible biotransformations at each step and guides the process to its conclusion. Mining the UM-BBD's data provides a unique view into how the microbial world recycles organic functional groups. UM-BBD users are encouraged to comment on all aspects of the database, including the information it contains and the tools by which it can be mined. The database and prediction system develop under the direction of the scientific community.

Biodegradation, Environmental↗
PubMed1988

Twenty-one-year experience with land mine injuries.

Land mines produce devastating injuries which are usually fatal. In Guantanamo Bay, there have been no survivors from close range, functioning antipersonnel mines of the M-16 series. All 15 antipersonnel mine fatalities suffered extremity amputation. Seven of the 15 patients suffered immediately fatal head, neck, or truncal injuries (Type I injury). The three patients who underwent hospital resuscitation had extremity amputation but were spared major head, neck, or truncal injury. It is in this group of injured that potentially salvageable patients can be identified; for them aggressive rescue and resuscitation must be performed. Those with Type II injuries are the highest priority in any triage plan. In a mass casualty or combat casualty scenario, Type II patients, in particular those with high bilateral above-the-knee amputations, may be reassigned to an expectant treatment category so as to allow the main focus on more salvageable patients. The prehospital management plan emphasizes rapid assessment and triage of patients, use of tourniquets to control extremity hemorrhage, supplemental oxygen or endotracheal intubation if possible, neck immobilization, use of the extremity section of the pneumatic antishock garment if applicable, and rapid transport to a hospital. Hospital management of these patients emphasizes aggressive resuscitation, early endotracheal intubation, and rapid volume replacement with simultaneous balanced salt solution and blood. Operative debridement with broad-spectrum antibiotic coverage and tetanus prophylaxis is performed; wounds are managed in an open fashion and frequently examined at subsequent dates in the operating room.

Amputation, Traumatic↗
PubMed1999

Below-knee amputations as a result of land-mine injuries: comparison of primary closure versus delayed primary closure.

BACKGROUND: Antipersonnel land mines are designed to maim by mutilating the lower extremities, and these injuries are at higher risk for infection than injuries from other weapon systems. METHODS: The results of 474 unilateral traumatic below-knee amputations as a result of land-mine injuries were reviewed. If the delay in evacuation between the injury and arrival to the battle field hospital was less than 6 hours, 392 amputation stumps (group I) were closed primarily after meticulous debridement. Open amputation was performed after debridement in the remaining 82 amputation stumps (group II), because there was a suspicion of ineffective debridement, although they were evacuated in less than 6 hours or delay was more than 6 hours. RESULTS: Eleven patients in group I (2.8%) were reoperated because of wound sepsis of the stump. Wound sepsis was not encountered in group II. A total of 87.4% of stumps in group I and 81.2% of stumps in group II had healed without a problem. No gas gangrene or tetanus was encountered in any cases. CONCLUSION: Our results reveal that primary closure may be done in traumatic below-knee amputations caused by land-mine injuries with an acceptable infection rate, if the evacuation time is less than 6 hours, and if there is meticulous debridement.

Adolescent↗
PubMedDate not supplied

Data mining methods find demographic predictors of preterm birth.

BACKGROUND: Preterm births in the United States increased from 11.0% to 11.4% between 1996 and 1997; they continue to be a complex healthcare problem in the United States. OBJECTIVE: The objective of this research was to compare traditional statistical methods with emerging new methods called data mining or knowledge discovery in databases in identifying accurate predictors of preterm births. METHOD: An ethnically diverse sample (N = 19,970) of pregnant women provided data (1,622 variables) for new methods of analysis. Preterm birth predictors were evaluated using traditional statistical and newer data mining analyses. RESULTS: Seven demographic variables (maternal age and binary coding for county of residence, education, marital status, payer source, race, and religion) yielded a .72 area under the curve using Receiving Operating Characteristic curves to test predictive accuracy. The addition of hundreds of other variables added only a .03 to the area under the curve. CONCLUSION: Similar results across data mining methods suggest that results are data-driven and not method-dependent, and that demographic variables offer a small set of parsimonious variables with reasonable accuracy in predicting preterm birth outcomes in a racially diverse population.

Data Collection↗
PubMed1988

Structuring strain data for storage and retrieval of information on fungi and yeasts in MINE, the Microbial Information Network Europe.

A distributed Microbial Information Network Europe (MINE) is being constructed by a number of major microbial culture collections in countries of the European Community, with the support of the Biotechnology Action Programme (BAP) of the Commission of the European Community. The representatives of the collections participating in MINE have agreed to adopt a general format for the computer storage and retrieval of strain data. This uniform format will facilitate the electronic combination and exchange of data from different collections in order to produce integrated catalogues and the use of identical commands to search the different databases. It is recommended to other collections who may wish to contribute data to the MINE network or between themselves. Three kinds of records can be linked to the leading 'species records': strain records, synonym records, and alternative morphonym records. A minimum data set of 30 fields (similar to the fields used for producing catalogues) is defined that facilitates the exchange of data between the national nodes and serves as a directory to strains available at other nodes. It is suggested that the full strain record comprise 99 fields, grouped in 12 blocks: internal administration--name--strain administration--status--environment and history--biological interactions--sexuality--properties (cytology, biomolecular data)--genotype and genetics--growth conditions--chemistry and enzymes--practical applications. Several fields are divided into subfields of different ranks. Delimiters are used either to separate a range of entries that have to be indexed or to divide an entry from the reference to its source or remarks that should not be indexed. The contents and structure of the fields proposed for filamentous fungi and yeasts are described and in some cases illustrated by examples. Uniformity of input is essential for indexed fields and desirable for non-indexed fields. Seven thesaurus files are envisaged to ensure consistency.

Data Collection↗
PubMed2000

Identifying potential tumor markers and antigens by database mining and rapid expression screening.

Genes expressed specifically in malignant tissue may have potential as therapeutic targets but have been difficult to locate for most cancers. The information hidden within certain public databases can reveal RNA transcripts specifically expressed in transformed tissue. To be useful, database information must be verified and a more complete pattern of tissue expression must be demonstrated. We tested database mining plus rapid screening by fluorescent-PCR expression comparison (F-PEC) as an approach to locate candidate brain tumor antigens. Cancer Genome Anatomy Project (CGAP) data was mined for genes highly expressed in glioblastoma multiforme. From 13 mined genes, seven showed potential as possible tumor markers or antigens as determined by further expression profiling. Now that large-scale expression information is readily available for many of the commonly occurring cancers, other candidate tumor markers or antigens could be located and evaluated with this approach.

Algorithms↗
PubMed2000

Autonomous decision-making: a data mining approach.

The researchers and practitioners of today create models, algorithms, functions, and other constructs defined in abstract spaces. The research of the future will likely be data driven. Symbolic and numeric data that are becoming available in large volumes will define the need for new data analysis techniques and tools. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. In this paper, a novel approach for autonomous decision-making is developed based on the rough set theory of data mining. The approach has been tested on a medical data set for patients with lung abnormalities referred to as solitary pulmonary nodules (SPNs). The two independent algorithms developed in this paper either generate an accurate diagnosis or make no decision. The methodolgy discussed in the paper depart from the developments in data mining as well as current medical literature, thus creating a variable approach for autonomous decision-making.

Algorithms↗
PubMed2004

Mining multilevel and location-aware service patterns in mobile web environments.

In this correspondence, we address the issue of efficiently mining multilevel and location-aware associated service patterns in a mobile web environment. In terms of multilevel concept, we consider the complex problem that locations and services are of hierarchical structures. We propose a new data mining method named two-dimensional multilevel (2-DML) association rules mining, which can efficiently discover the associated service request patterns by taking into account the multilevel properties of locations and services. The discovered patterns can be effectively utilized in real applications like location-based and personalized services. To the best of our knowledge, this is the first work addressing this research issue. Some variations of the 2-DML method with different properties in terms of execution efficiency and memory efficiency were also developed. Through empirical evaluation, the proposed methods are shown to deliver good performance in terms of efficiency and scalability under various system conditions.

Algorithms↗
PubMed2000

Occurrence of sulfate-reducing bacteria under a wide range of physico-chemical conditions in Au and Cu-Zn mine tailings.

Sulfate-reducing bacteria (SRB) have been observed in mining environments, but their presence has not been linked to specific physico-chemical and mineralogical factors. The present study was undertaken to assess the presence of SRB in several Au and Cu-Zn mine tailings located near Timmins, Ont., Canada, and determine the factors responsible for their presence. Vegetated and non-vegetated mine tailings were sampled for SRB enumeration, pH, Eh, water content, total carbon content and sequential chemical extraction. Results first showed that SRB populations were present at all sites and that their distribution varied with depth. Populations were recovered from neutral pH and slightly anoxic tailings and from highly acidic (pH 2) and oxic tailings. The total carbon content of the tailings was generally low and not related to the presence of vegetation. In addition, the carbon content did not affect SRB population distribution and appeared to be more related to the type of tailings, i.e., oxidized and acidic Cu-Zn tailings contained on average more carbon than Au tailings. Results also indicated that the water content of the tailings varied greatly with depth and was not related to the presence of SRB populations. The sequential chemical extraction showed that the pyrite content of the tailings was lower in Au tailings than in Cu-Zn tailings, and that some oxidized Cu-Zn sites were depleted in pyrite due to microbial and chemical oxidation. Our results indicate that SRB could be cultured from a variety of sites and sample types, and that factors such as pH, Eh, water content and carbon content at the collection sites did not exert control on their presence.

Journal Article↗
PubMed1981

Acidophilic, heterotrophic bacteria of acidic mine waters.

Obligately acidophilic, heterotrophic bacteria were isolated both from enrichment cultures developed with acidic mine water and from natural mine drainage. The bacteria were grouped by the ability to utilize a number of organic acids as sole carbon sources. None of the strains were capable of chemolithotrophic growth on inorganic reduced iron and sulfur compounds. All bacteria were rod shaped, gram negative, nonencapsulated, motile, capable of growth at pH 2.6 but not at pH 6.0, catalase and oxidase positive, strictly aerobic, and capable of growth on citric acid. The bacteria were cultivatable on solid nutrient media only if agarose was employed as the hardening agent. Bacterial densities in natural mine waters ranged from approximately 20 to 250 cells per ml, depending upon source and culture medium. Ferric hydrates and stream vegetation contained from 1,500 to over 7 x 10 cells per g.

Journal Article↗
PubMed2005

Online analytical processing (OLAP): a fast and effective data mining tool for gene expression databases.

Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.

Journal Article↗
PubMed2000

Data mining in brain imaging.

Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of task-activation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed.

Algorithms↗
PubMed2005

Storing, linking, and mining microarray databases using SRS.

BACKGROUND: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. RESULTS: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. CONCLUSION: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases.

Computational Biology↗
PubMed2006

A data mining approach in home healthcare: outcomes and service use.

BACKGROUND: The purpose of this research is to understand the performance of home healthcare practice in the US. The relationships between home healthcare patient factors and agency characteristics are not well understood. In particular, discharge destination and length of stay have not been studied using a data mining approach which may provide insights not obtained through traditional statistical analyses. METHODS: The data were obtained from the 2000 National Home and Hospice Care Survey data for three specific conditions (chronic obstructive pulmonary disease, heart failure and hip replacement), representing nearly 580 patients from across the US. The data mining approach used was CART (Classification and Regression Trees). Our aim was twofold: 1) determining the drivers of home healthcare service outcomes (discharge destination and length of stay) and 2) examining the applicability of induction through data mining to home healthcare data. RESULTS: Patient age (85 and older) was a driving force in discharge destination and length of stay for all three conditions. There were also impacts from the type of agency, type of payment, and ethnicity. CONCLUSION: Patients over 85 years of age experience differential outcomes depending on the condition. There are also differential effects related to agency type by condition although length of stay was generally lower for hospital-based agencies. The CART procedure was sufficiently accurate in correctly classifying patients in all three conditions which suggests continuing utility in home health care.

Aftercare↗
PubMed2006

Epidemiological geomatics in evaluation of mine risk education in Afghanistan: introducing population weighted raster maps.

Evaluation of mine risk education in Afghanistan used population weighted raster maps as an evaluation tool to assess mine education performance, coverage and costs. A stratified last-stage random cluster sample produced representative data on mine risk and exposure to education. Clusters were weighted by the population they represented, rather than the land area. A "friction surface" hooked the population weight into interpolation of cluster-specific indicators. The resulting population weighted raster contours offer a model of the population effects of landmine risks and risk education. Five indicator levels ordered the evidence from simple description of the population-weighted indicators (level 0), through risk analysis (levels 1-3) to modelling programme investment and local variations (level 4). Using graphic overlay techniques, it was possible to metamorphose the map, portraying the prediction of what might happen over time, based on the causality models developed in the epidemiological analysis. Based on a lattice of local site-specific predictions, each cluster being a small universe, the "average" prediction was immediately interpretable without losing the spatial complexity.

Afghanistan↗
PubMed2003

A semiautomated approach to gene discovery through expressed sequence tag data mining: discovery of new human transporter genes.

Identification and functional characterization of the genes in the human genome remain a major challenge. A principal source of publicly available information used for this purpose is the National Center for Biotechnology Information database of expressed sequence tags (dbEST), which contains over 4 million human ESTs. To extract the information buried in this data more effectively, we have developed a semiautomated method to mine dbEST for uncharacterized human genes. Starting with a single protein input sequence, a family of related proteins from all species is compiled. This entire family is then used to mine the human EST database for new gene candidates. Evaluation of putative new gene candidates in the context of a family of characterized proteins provides a framework for inference of the structure and function of the new genes. When applied to a test data set of 28 families within the major facilitator superfamily (MFS) of membrane transporters, our protocol found 73 previously characterized human MFS genes and 43 new MFS gene candidates. Development of this approach provided insights into the problems and pitfalls of automated data mining using public databases.

Biological Transport, Active↗
PubMed2006

Imitating manual curation of text-mined facts in biomedicine.

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

Abstracting and Indexing↗
PubMed2004

Microarray data analysis and mining.

DNA microarray is an innovative technology for obtaining information on gene function. Because it is a high-throughput method, computational tools are essential in data analysis and mining to extract the knowledge from experimental results. Filtering procedures and statistical approaches are frequently combined to identify differentially expressed genes. However, obtaining a list of differentially expressed genes is only the starting point because an important step is the integration of differential expression profiles in a biological context, which is a hot topic in data mining. In this chapter an integrated approach of filtering and statistical validation to select trustable differentially expressed genes is described together with a brief introduction on data mining focusing on the classification of co-regulated genes on the basis of their biological function.

Cluster Analysis↗