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PubMed2006

Literature mining for the biologist: from information retrieval to biological discovery.

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.

Computational Biology↗

PubMed1999

Topological regulation of cell division in Escherichia coli involves rapid pole to pole oscillation of the division inhibitor MinC under the control of MinD and MinE.

Placement of the Z ring at midcell in Escherichia coli is assured by the action of the min system, which blocks usage of potential division sites that exist at the cell poles. This activity of min is achieved through the action of an inhibitor of division, MinC, that is activated by MinD and topologically regulated by MinE. In this study, we have used a functional GFP-MinC fusion to monitor the location of MinC. We find that GFP-MinC is a cytoplasmic protein in the absence of the other Min proteins. The addition of MinD, a peripheral membrane protein that interacts with MinC, results in GFP-MinC appearing on the membrane. In the presence of both MinD and MinE, GFP-MinC oscillates rapidly between the halves of the cell. Thus, MinC is positioned by the other Min products, but in a dynamic manner so that it is in position to inhibit Z ring assembly away from midcell.

Adenosine Triphosphatases↗

PubMed2002

Conservation of dynamic localization among MinD and MinE orthologues: oscillation of Neisseria gonorrhoeae proteins in Escherichia coli.

Min proteins are involved in the correct placement of division septa in many bacterial species. In Escherichia coli (Ec) cells, these proteins oscillate from pole to pole, ostensibly to prevent unwanted polar septation. Here, we show that Min proteins from the coccus Neisseria gonorrhoeae (Ng) also oscillate in E. coli. Green fluorescent protein (GFP) fusions to gonococcal MinD and MinE localized dynamically in different E. coli backgrounds. GFP-MinDNg moved from pole to pole in rod-shaped E. coli cells with a 70 +/- 25 s localization cycle when MinENg was expressed in cis. The oscillation time of GFP-MinDNg was reduced when wild-type MinENg was replaced with MinENg carrying a R30D mutation, but lengthened by 15 s when activated by MinEEc. Several mutations in the N-terminal domain of MinDNg, including K16Q and 4- and 19-amino acid truncations, prevented oscillation; these MinDNg mutants showed decreased or lost interaction with themselves and MinENg. Like MinEEc-GFP, MinENg-GFP formed MinE rings and oscillated in E. coli cells when MinDEc was expressed in cis. Finally, in round E. coli cells, GFP-MinDNg appeared to move in a plane parallel to completed septa. This pattern of movement is predicted to be similar in gonococcal cells, which also divide in alternating perpendicular planes.

Adenosine Triphosphatases↗

PubMed2003

Dynamic structures in Escherichia coli: spontaneous formation of MinE rings and MinD polar zones.

In Escherichia coli, division site selection is regulated in part by the Min-protein system. Oscillations of the Min proteins from pole to pole every approximately 40 sec have been revealed by in vivo studies of GFP fusions. The dynamic oscillatory structures produced by the Min proteins, including a ring of MinE protein, compact polar zones of MinD, and zebra-striped oscillations in filamentous cells, remain unexplained. We show that the Min oscillations, including mutant phenotypes, can be accounted for by in vitro-observed interactions involving MinD and MinE, with a crucial role played by the rate of nucleotide exchange. Recent discoveries suggest that protein oscillations may play a general role in proper chromosome and plasmid partitioning.

Adenosine Triphosphatases↗

PubMed1978

Comparison of respirable coal mine dust concentrations measured with an MRE and a newly-developed, two-stage impactor sampler.

The design and laboratory evaluation of a two-stage aerosol sampler using an impaction plate for removing particles above 10 micron, has been reported. The unit was developed to provide an alternative to the two-stage sampling head used on approved coal mine personal respirable dust samplers. The new device offers the potential of increasing the analytical accuracy of samples, and of providing a device more suitable to the mining community for use in their mandated dust sampling program. This paper describes the underground sampling program conducted to establish the factor for converting respirable dust concentrations measured with this device to equivalent concentrations measured with an Isleworth 113A gravimetric dust sampler (MRE). This study demonstrated that the use of the impactor sampling head provides a viable alternative to the currently used sampling head. The results showed that comparative measurements could be linearly related for MRE dust measurements ranging from 0.5 to 5.5 mg/m3. The factor determined for converting measurements made with the new head to equivalent MRE measurements was approximately 0.5.

Coal↗

PubMedDate not supplied

Atomic absorption spectrophotometric and X-ray studies of respirable dusts in Indian coal mines.

Quantitative determination of 10 minor and 8 trace elements in respirable coal dust by atomic absorption spectrophotometry is described herein. The coal dust samples were collected in the mine atmosphere during drilling in coal seams. A "Hexhlet" apparatus specially designed and fitted with a horizontal elutriator was used to collect the respirable coal dust fraction. After destruction of organic matter by wet oxidation and filtering off the clay and silica, iron, calcium, magnesium, sodium, potassium, manganese, zinc, copper, cadmium, and nickel were determined directly in the resulting solution by atomic absorption spectrophotometry. The studies relate to the respiratory disease-pneumoconiosis-affecting coal mine workers. X-Ray diffraction studies have shown the presence of kaolin, quartz, pirrsonite and beidellite clay minerals in the coal dust.

Coal↗

PubMed2000

A general 13C NMR spectrum predictor using data mining techniques.

A general-case neural network model for 13C NMR spectrum prediction (estimation) was built from more than 8,300 carbon atoms having various environments. Building the model from the data set required a few weeks' work using commercial software. Average deviation on test data is ca. 4 ppm. There is no limit on molecule complexity. Estimation error does not depend on molecule size or complexity. The emphasis is on the data, the method and the results, not on the processes that take place inside the modelling software. Advantages, disadvantages and peculiarities of neural network-based data modelling ("data mining") are described at length. The differences in data handling between the data mining approach and traditional statistical modelling techniques are discussed and illustrated in detail. The spectrum predictor is available from PMSI at no charge.

Carbon Isotopes↗

PubMedDate not supplied

Data mining techniques applied to medical information.

Knowledge discovery from the dramatically increased data of an auto-stored medical information system is still in its infancy. The purpose of this study is to use widely available and easily operated techniques that can satisfy general users in extracting specific knowledge to make the medical information system more functional. Data mining techniques, including data visualisation, correlation analysis, discriminant analysis, and neural networks supervised classification, were applied to heart disease databases. These techniques can help to identify high risk patients, define the most important factors (variables) in heart disease, and build a multivariate relationship model to show the relationship between any two variables in a way that such relationships are easy to view. Simple visualization techniques were utilised to construct this model, which corresponds with current medical knowledge. Two nonparametric (distribution assumption free) classification tools were employed to identify high risk heart disease patients. Both the neural networks supervised classification methods and the discriminant analysis method produced reliable classification rates for heart disease patients. However, neural networks yielded a higher percentage of correct classifications (averaging 89%) than discriminant analysis (79%). Data visualisation and correlation analysis resulted in similar conclusions regarding the most important factors in heart disease. These data mining tools provide simple and effective methods of extracting knowledge from general medical information. The treatment of missing data is also discussed.

Computer Graphics↗

PubMed2002

Appropriate medical data categorization for data mining classification techniques.

Some data mining (DM) methods, or software tools, require normalized data, others rely on categorized data, and some can accommodate multiple data scales. Each DM technique has a specific background theory; therefore, different results are expected when applying multiple methods. The purpose of this study is to find the data format appropriate for each DM classification technique for wider applications, and efficiently to obtain trustworthy results. Considering the nature of medical data, categorical variables are sometimes useful for making decisions and can make it easier to extrapolate knowledge. In this study, three mathematical data categorization methods (Fusinter, minimum description length principle [MDLPC] and Chi-merge) were applied to accommodate five data mining classification techniques (statistics discriminant analysis, supervised classification with Neural Networks, Decision trees, Genetic supervised clustering and Bayesian classification [probability neural networks; PNN]) using a heart disease database with four types of data (continuous data, binary data, nominal data, and ordinal data). Compared with original or normalized data, data categorized by the MDLPC categorization method was found to perform better in most of the DM classification techniques used in this study. Categorical data is good for most DM classification techniques (e.g. classification of disease and non-disease groups) and is relatively easy to use for extracting medical knowledge.

Bayes Theorem↗

PubMed2000

Data mining applied to linkage disequilibrium mapping.

We introduce a new method for linkage disequilibrium mapping: haplotype pattern mining (HPM). The method, inspired by data mining methods, is based on discovery of recurrent patterns. We define a class of useful haplotype patterns in genetic case-control data and use the algorithm for finding disease-associated haplotypes. The haplotypes are ordered by their strength of association with the phenotype, and all haplotypes exceeding a given threshold level are used for prediction of disease susceptibility-gene location. The method is model-free, in the sense that it does not require (and is unable to utilize) any assumptions about the inheritance model of the disease. The statistical model is nonparametric. The haplotypes are allowed to contain gaps, which improves the method's robustness to mutations and to missing and erroneous data. Experimental studies with simulated microsatellite and SNP data show that the method has good localization power in data sets with large degrees of phenocopies and with lots of missing and erroneous data. The power of HPM is roughly identical for marker maps at a density of 3 single-nucleotide polymorphisms/cM or 1 microsatellite/cM. The capacity to handle high proportions of phenocopies makes the method promising for complex disease mapping. An example of correct disease susceptibility-gene localization with HPM is given with real marker data from families from the United Kingdom affected by type 1 diabetes. The method is extendable to include environmental covariates or phenotype measurements or to find several genes simultaneously.

Adolescent↗

PubMedDate not supplied

Comparing graph representations of protein structure for mining family-specific residue-based packing motifs.

We find recurring amino-acid residue packing patterns, or spatial motifs, that are characteristic of protein structural families, by applying a novel frequent subgraph mining algorithm to graph representations of protein three-dimensional structure. Graph nodes represent amino acids, and edges are chosen in one of three ways: first, using a threshold for contact distance between residues; second, using Delaunay tessellation; and third, using the recently developed almost-Delaunay edges. For a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, subgraph mining typically identifies several hundred common subgraphs corresponding to spatial motifs that are frequently found in proteins in the family but rarely found outside of it. We find that some of the large motifs map onto known functional regions in two protein families explored in this study, i.e., serine proteases and kinases. We find that graphs based on almost-Delaunay edges significantly reduce the number of edges in the graph representation and hence present computational advantage, yet the patterns extracted from such graphs have a biological interpretation approximately equivalent to that of those extracted from distance based graphs.

Algorithms↗

PubMed2005

Text mining and ontologies in biomedicine: making sense of raw text.

The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.

Abstracting and Indexing↗

PubMed2005

Evaluation of biomedical text-mining systems: lessons learned from information retrieval.

Biomedical text-mining systems have great promise for improving the efficiency and productivity of biomedical researchers. However, such systems are still not in routine use. One impediment to their development is the lack of systematic and rigorous evaluation, comparable to the approaches developed for information retrieval systems. The developers of text-mining systems need to improve both test collections for system-oriented evaluation and undertake user-oriented evaluations to determine the most effective use of their systems for their intended audience.

Algorithms↗

PubMed2006

Mining literature for systems biology.

Currently, literature is integrated in systems biology studies in three ways. Hand-curated pathways have been sufficient for assembling models in numerous studies. Second, literature is frequently accessed in a derived form, such as the concepts represented by the Medical Subject Headings (MeSH) and Gene Ontologies (GO), or functional relationships captured in protein-protein interaction (PPI) databases; both of these are convenient, consistent reductions of more complex concepts expressed as free text in the literature. Moreover, their contents are easily integrated into computational processes required for dealing with large data sets. Last, mining text directly for specific types of information is on the rise as text analytics methods become more accurate and accessible. These uses of literature, specifically manual curation, derived concepts captured in ontologies and databases, and indirect and direct application of text mining, will be discussed as they pertain to systems biology.

Animals↗

PubMed1998

The Merck Gene Index browser: an extensible data integration system for gene finding, gene characterization and EST data mining.

MOTIVATION: To make effective use of the vast amounts of expressed sequence tag (EST) sequence data generated by the Merck-sponsored EST project and other similar efforts, sequences must be organized into gene classes, and scientists must be able to 'mine' the gene class data in the context of related genomic data. RESULTS: This paper presents the Merck Gene Index browser, an easily extensible, World Wide Web-based system for mining the Merck Gene Index (MGI) and related genomic data. The MGI is a non-redundant set of clones and sequences, each representing a distinct gene, constructed from all high-quality 3' EST sequences generated by the Merck-sponsored EST project. The MGI browser integrates data from a variety of sources and storage formats, both local and remote, using an eclectic integration strategy, including a federation of relational databases, a local data warehouse and simple hypertext links. Data currently integrated include: LENS cDNA clone and EST data, dbEST protein and non-EST nucleic acid similarity data, WashU sequence chromatograms. Entrez sequence and Medline entries, and UniGene gene clusters. Flatfile sequence data are accessed using the Bioapps server, an internally developed client-server system that supports generic sequence analysis applications. Browser data are retrieved and formatted by means of the Bioinformatics Data Integration Toolkit (B-DIT), a new suite of Perl routines.

Abstracting and Indexing↗

PubMed2001

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT.

MOTIVATION: The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations. RESULTS: A standard data mining algorithm was successfully applied to gain knowledge about the Keyword annotation in SWISS-PROT. 11 306 rules were generated, which are provided in a database and can be applied to yet unannotated protein sequences and viewed using a web browser. They rely on the taxonomy of the organism, in which the protein was found and on signature matches of its sequence. The statistical evaluation of the generated rules by cross-validation suggests that by applying them on arbitrary proteins 33% of their keyword annotation can be generated with an error rate of 1.5%. The coverage rate of the keyword annotation can be increased to 60% by tolerating a higher error rate of 5%. AVAILABILITY: The results of the automatic data mining process can be browsed on http://golgi.ebi.ac.uk:8080/Spearmint/ Source code is available upon request. CONTACT: kretsch@ebi.ac.uk.

Algorithms↗

PubMed2002

Accomplishments and challenges in literature data mining for biology.

We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to a range of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.

Benchmarking↗

PubMed2003

The small-world dynamics of tree networks and data mining in phyloinformatics.

MOTIVATION: A noble and ultimate objective of phyloinformatic research is to assemble, synthesize, and explore the evolutionary history of life on earth. Data mining methods for performing these tasks are not yet well developed, but one avenue of research suggests that network connectivity dynamics will play an important role in future methods. Analysis of disordered networks, such as small-world networks, has applications as diverse as disease propagation, collaborative networks, and power grids. Here we apply similar analyses to networks of phylogenetic trees in order to understand how synthetic information can emerge from a database of phylogenies. RESULTS: Analyses of tree network connectivity in TreeBASE show that a collection of phylogenetic trees behaves as a small-world network-while on the one hand the trees are clustered, like a non-random lattice, on the other hand they have short characteristic path lengths, like a random graph. Tree connectivities follow a dual-scale power-law distribution (first power-law exponent approximately 1.87; second approximately 4.82). This unusual pattern is due, in part, to the presence of alternative tree topologies that enter the database with each published study. As expected, small collections of trees decrease connectivity as new trees are added, while large collections of trees increase connectivity. However, the inflection point is surprisingly low: after about 600 trees the network suddenly jumps to a higher level of coherence. More stringent definitions of 'neighbour' greatly delay the threshold whence a database achieves sufficient maturity for a coherent network to emerge. However, more stringent definitions of 'neighbour' would also likely show improved focus in data mining. AVAILABILITY: http://treebase.org

Algorithms↗