Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Data mining”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,135 records · Page 63Linked to original sources
PubMed2006

Statistical methods in genetics.

In recent years, a very large variety of statistical methodologies, at various levels of complexity, have been put forward to analyse genotype data and detect genetic variations that may be responsible for increasing the susceptibility to disease. This review provides a concise account of a number of selected statistical methods for population-based association mapping, from single-marker tests of association to multi-marker data mining techniques for gene-gene interaction detection.

Genetic Markers↗
PubMed1999

The domain-server: direct prediction of protein domain-homologies from BLAST search.

RESULTS: A WWW server for protein domain homology prediction, based on BLAST search and a simple data-mining algorithm (Hegyi,H. and Pongor,S. (1993) Comput. Appl. Biosci., 9, 371-372), was constructed providing a tabulated list and a graphic plot of similarities. AVAILABILITY: http://www.icgeb.trieste.it/domain. Mirror site is available at http://sbase.abc.hu/domain. A standalone programme will be available on request. SUPPLEMENTARY INFORMATION: A series of help files is available at the above addresses.

Algorithms↗
PubMed2000

The complexities of genome analysis, the Retroid agent perspective.

MOTIVATION: The sequences of Retroid agents from a wide diversity of organisms constitute the largest set of complete genomes currently available for the study of genomic architecture and the transfer of information within and between organisms. These agents are ubiquitous in Eukaryotes, comprising 50-90% of the genomic information in some cases. RESULTS: Analyses conducted for over a decade illustrate that Retroid agents are engaged in a wide spectrum of molecular evolutionary events. A description of these complexities is presented as a three parameter conceptual framework that considers type, size, and mechanism of events that contribute to the evolution of genes, genomes, and organisms. The results of new data mining studies further illustrate the complexity of the network of relationships among and between Retroid agents and other organisms. A hidden Markov model construction strategy is presented that generates a multiple alignment more similar to those refined by human experts. CONTACT: mars@parvati.msu.montana. edu

Animals↗
PubMed2000

GABAagent: a system for integrating data on GABA receptors.

MOTIVATION: Scientific data pertaining to GABA receptors, which are of medical importance, are widely scattered throughout numerous heterogeneous Internet resources. This situation has made the integrated acquisition of such data difficult and substantially time consuming even for researchers who are Internet aficionados. Thus, there exists a genuine need for the development of Internet applications, such as GABAagent, which provide efficient and timely access to concise and integrated information. RESULTS: We report here the establishment of a novel server (GABAagent) which has been written in Perl script, and which is freely accessible through the Internet. GABAagent is designed to assist researchers in retrieving focused and integrated information related to GABA receptors from various public domain databases. GABAagent relies on server-side flat-file databases that have been created through data mining from Internet sources such as the PubMed, DDBJ, SWISS-PROT and TrEMBL, in addition to the many World Wide Web (Web) sites which are accessible through Excite (E-Web). These warehouse databases are regularly updated and contain among other things, information concerning: (i) GABA receptor publications, (ii) DNA and protein sequences and (iii) the contents of related E-Web sites along with their addresses. Our system also provides hard links to the above-mentioned Web sites and E-Web sites; the feature which adds to it the character of virtual federation type of database. The current version of GABAagent provides two user-friendly services. The first is a search engine possessing intelligent query reformulation support (GABAengine), the second an elaborate email alert service was designed into the system (GABAalert). The GABAengine allows the user to search server-side databases exclusively for GABA receptor-related queries. Whereas, GABAalert allows the user, by means of subscription, to receive immediate and/or monthly updates automatically. AVAILABILITY: GABAagent is freely accessible at the following Web address http://www.ust.hk/gaba.

Databases, Factual↗
PubMed2001

Use of keyword hierarchies to interpret gene expression patterns.

MOTIVATION: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. RESULTS: We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.

Abstracting and Indexing↗
PubMed2001

The utility of different representations of protein sequence for predicting functional class.

MOTIVATION: Data Mining Prediction (DMP) is a novel approach to predicting protein functional class from sequence. DMP works even in the absence of a homologous protein of known function. We investigate the utility of different ways of representing protein sequence in DMP (residue frequencies, phylogeny, predicted structure) using the Escherichia coli genome as a model. RESULTS: Using the different representations DMP learnt prediction rules that were more accurate than default at every level of function using every type of representation. The most effective way to represent sequence was using phylogeny (75% accuracy and 13% coverage of unassigned ORFs at the most general level of function: 69% accuracy and 7% coverage at the most detailed). We tested different methods for combining predictions from the different types of representation. These improved both the accuracy and coverage of predictions, e.g. 40% of all unassigned ORFs could be predicted at an estimated accuracy of 60% and 5% of unassigned ORFs could be predicted at an estimated accuracy of 86%.

Bacterial Proteins↗
PubMed2001

Prediction of the coupling specificity of G protein coupled receptors to their G proteins.

UNLABELLED: G protein coupled receptors (GPCRs) are found in great numbers in most eukaryotic genomes. They are responsible for sensing a staggering variety of structurally diverse ligands, with their activation resulting in the initiation of a variety of cellular signalling cascades. The physiological response that is observed following receptor activation is governed by the guanine nucleotide-binding proteins (G proteins) to which a particular receptor chooses to couple. Previous investigations have demonstrated that the specificity of the receptor-G protein interaction is governed by the intracellular domains of the receptor. Despite many studies it has proven very difficult to predict de novo, from the receptor sequence alone, the G proteins to which a GPCR is most likely to couple. We have used a data-mining approach, combining pattern discovery with membrane topology prediction, to find patterns of amino acid residues in the intracellular domains of GPCR sequences that are specific for coupling to a particular functional class of G proteins. A prediction system was then built, being based upon these discovered patterns. We can report this approach was successful in the prediction of G protein coupling specificity of unknown sequences. Such predictions should be of great use in providing in silico characterisation of newly cloned receptor sequences and for improving the annotation of GPCRs stored in protein sequence databases. AVAILABILITY: http://www.ebi.ac.uk/~croning/coupling.html.

Amino Acid Sequence↗
PubMed2002

A tree kernel to analyse phylogenetic profiles.

MOTIVATION: The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every fully sequenced genome. Because proteins that participate in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion, the phylogenetic profiles of such proteins are often 'similar' or at least 'related' to each other. The question we address in this paper is the following: how to measure the 'similarity' between two profiles, in an evolutionarily relevant way, in order to develop efficient function prediction methods? RESULTS: We show how the profiles can be mapped to a high-dimensional vector space which incorporates evolutionarily relevant information, and we provide an algorithm to compute efficiently the inner product in that space, which we call the tree kernel. The tree kernel can be used by any kernel-based analysis method for classification or data mining of phylogenetic profiles. As an application a Support Vector Machine (SVM) trained to predict the functional class of a gene from its phylogenetic profile is shown to perform better with the tree kernel than with a naive kernel that does not include any information about the phylogenetic relationships among species. Moreover a kernel principal component analysis (KPCA) of the phylogenetic profiles illustrates the sensitivity of the tree kernel to evolutionarily relevant variations.

Algorithms↗
PubMed2003

AVA: visual analysis of gene expression microarray data.

SUMMARY: AVA (Array Visual Analyzer) is a Java program that provides a graphical environment for visualization and analysis of gene expression microarray data. Together with its interactive visualization tools and a variety of built-in data analysis and filtration methods, AVA effectively integrates microarray data normalization, quality assessment, and data mining into one application. AVAILABILITY: The software is freely available for academic users on request from the authors.

Algorithms↗
PubMed2003

LTR_STRUC: a novel search and identification program for LTR retrotransposons.

MOTIVATION: Long terminal repeat (LTR) retrotransposons constitute a substantial fraction of most eukaryotic genomes and are believed to have a significant impact on genome structure and function. Conventional methods used to search for LTR retrotransposons in genome databases are labor intensive. We present an efficient, reliable and automated method to identify and analyze members of this important class of transposable elements. RESULTS: We have developed a new data-mining program, LTR_STRUC (LTR retrotransposon structure program) which identifies and automatically analyzes LTR retrotransposons in genome databases by searching for structural features characteristic of such elements. LTR_STRUC has significant advantages over conventional search methods in the case of LTR retrotransposon families having low sequence homology to known queries or families with atypical structure (e.g. non-autonomous elements lacking canonical retroviral ORFs) and is thus a discovery tool that complements established methods. LTR_STRUC finds LTR retrotransposons using an algorithm that encompasses a number of tasks that would otherwise have to be initiated individually by the user. For each LTR retrotransposon found, LTR_STRUC automatically generates an analysis of a variety of structural features of biological interest. AVAILABILITY: The LTR_STRUC program is currently available as a console application free of charge to academic users from the authors.

Algorithms↗
PubMed2004

Extracting human protein interactions from MEDLINE using a full-sentence parser.

MOTIVATION: The living cell is a complex machine that depends on the proper functioning of its numerous parts, including proteins. Understanding protein functions and how they modify and regulate each other is the next great challenge for life-sciences researchers. The collective knowledge about protein functions and pathways is scattered throughout numerous publications in scientific journals. Bringing the relevant information together becomes a bottleneck in a research and discovery process. The volume of such information grows exponentially, which renders manual curation impractical. As a viable alternative, automated literature processing tools could be employed to extract and organize biological data into a knowledge base, making it amenable to computational analysis and data mining. RESULTS: We present MedScan, a completely automated natural language processing-based information extraction system. We have used MedScan to extract 2976 interactions between human proteins from MEDLINE abstracts dated after 1988. The precision of the extracted information was found to be 91%. Comparison with the existing protein interaction databases BIND and DIP revealed that 96% of extracted information is novel. The recall rate of MedScan was found to be 21%. Additional experiments with MedScan suggest that MEDLINE is a unique source of diverse protein function information, which can be extracted in a completely automated way with a reasonably high precision. Further directions of the MedScan technology improvement are discussed. AVAILABILITY: MedScan is available for commercial licensing from Ariadne Genomics, Inc.

Abstracting and Indexing↗
PubMed2004

ESPD: a pattern detection model underlying gene expression profiles.

MOTIVATION: DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in high-dimensional gene space. Furthermore, most of the genes collected may not necessarily be of interest and uncertainty about which genes are relevant makes it difficult to construct an informative gene space. Unsupervised empirical sample pattern discovery and informative genes identification of such sparse high-dimensional datasets present interesting but challenging problems. RESULTS: A new model called empirical sample pattern detection (ESPD) is proposed to delineate pattern quality with informative genes. By integrating statistical metrics, data mining and machine learning techniques, this model dynamically measures and manipulates the relationship between samples and genes while conducting an iterative detection of informative space and the empirical pattern. The performance of the proposed method with various array datasets is illustrated.

Algorithms↗
PubMed2004

CEBS object model for systems biology data, SysBio-OM.

MOTIVATION: To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledge base is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and protein-protein interaction analysis (proteomics) and changes in low molecular weight metabolite levels (metabolomics). RESULTS: To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, Systems Biology Object Model (SysBio-OM). The model is comprehensive and leverages other open source efforts, namely the MicroArray Gene Expression Object Model (MAGE-OM) and the Proteomics Experiment Data Repository (PEDRo) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction and metabolomics data. SysBio-OM promotes the standardization of data representation and data quality by facilitating the capture of the minimum annotation required for an experiment. Such standardization refines the accuracy of data mining and interpretation. The open source SysBio-OM model, which can be implemented on varied computing platforms is presented here. AVAILABILITY: A universal modeling language depiction of the entire SysBio-OM is available at http://cebs.niehs.nih.gov/SysBioOM/. The Rational Rose object model package is distributed under an open source license that permits unrestricted academic and commercial use and is available at http://cebs.niehs.nih.gov/cebsdownloads. The database and interface are being built to implement the model and will be available for public use at http://cebs.niehs.nih.gov.

Database Management Systems↗
PubMed2004

THEA: ontology-driven analysis of microarray data.

MOTIVATION: Microarray technology makes it possible to measure thousands of variables and to compare their values under hundreds of conditions. Once microarray data are quantified, normalized and classified, the analysis phase is essentially a manual and subjective task based on visual inspection of classes in the light of the vast amount of information available. Currently, data interpretation clearly constitutes the bottleneck of such analyses and there is an obvious need for tools able to fill the gap between data processed with mathematical methods and existing biological knowledge. RESULTS: THEA (Tools for High-throughput Experiments Analysis) is an integrated information processing system allowing convenient handling of data. It allows to automatically annotate data issued from classification systems with selected biological information coming from a knowledge base and to either manually search and browse through these annotations or automatically generate meaningful generalizations according to statistical criteria (data mining). AVAILABILITY: The software is available on the website http://thea.unice.fr/

Abstracting and Indexing↗
PubMed2004

Discovering patterns to extract protein-protein interactions from full texts.

MOTIVATION: Although there are several databases storing protein-protein interactions, most such data still exist only in the scientific literature. They are scattered in scientific literature written in natural languages, defying data mining efforts. Much time and labor have to be spent on extracting protein pathways from literature. Our aim is to develop a robust and powerful methodology to mine protein-protein interactions from biomedical texts. RESULTS: We present a novel and robust approach for extracting protein-protein interactions from literature. Our method uses a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions. A matching algorithm is designed to extract the interactions between proteins. Equipped only with a dictionary of protein names, our system achieves a recall rate of 80.0% and precision rate of 80.5%. AVAILABILITY: The program is available on request from the authors.

Algorithms↗
PubMed2004

Protein names precisely peeled off free text.

MOTIVATION: Automatically identifying protein names from the scientific literature is a pre-requisite for the increasing demand in data-mining this wealth of information. Existing approaches are based on dictionaries, rules and machine-learning. Here, we introduced a novel system that combines a pre-processing dictionary- and rule-based filtering step with several separately trained support vector machines (SVMs) to identify protein names in the MEDLINE abstracts. RESULTS: Our new tagging-system NLProt is capable of extracting protein names with a precision (accuracy) of 75% at a recall (coverage) of 76% after training on a corpus, which was used before by other groups and contains 200 annotated abstracts. For our estimate of sustained performance, we considered partially identified names as false positives. One important issue frequently ignored in the literature is the redundancy in evaluation sets. We suggested some guidelines for removing overly inadequate overlaps between training and testing sets. Applying these new guidelines, our program appeared to significantly out-perform other methods tagging protein names. NLProt was so successful due to the SVM-building blocks that succeeded in utilizing the local context of protein names in the scientific literature. We challenge that our system may constitute the most general and precise method for tagging protein names. AVAILABILITY: http://cubic.bioc.columbia.edu/services/nlprot/

Abstracting and Indexing↗
PubMed2005

PLATCOM: a Platform for Computational Comparative Genomics.

MOTIVATION: As more whole genome sequences become available, comparing multiple genomes at the sequence level can provide insight into new biological discovery. However, there are significant challenges for genome comparison. The challenge includes requirement for computational resources owing to the large volume of genome data. More importantly, since the choice of genomes to be compared is entirely subjective, there are too many choices for genome comparison. For these reasons, there is pressing need for bioinformatics systems for comparing multiple genomes where users can choose genomes to be compared freely. RESULTS: PLATCOM (Platform for Computational Comparative Genomics) is an integrated system for the comparative analysis of multiple genomes. The system is built on several public databases and a suite of genome analysis applications are provided as exemplary genome data mining tools over these internal databases. Researchers are able to visually investigate genomic sequence similarities, conserved gene neighborhoods, conserved metabolic pathways and putative gene fusion events among a set of selected multiple genomes. AVAILABILITY: http://platcom.informatics.indiana.edu/platcom

Chromosome Mapping↗
PubMed2005

BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.

biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.

Algorithms↗