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Biomedical subjects

Jonathan D Wren

Publications and source records attributed to Jonathan D Wren.

29 records · Page 2Linked to original sources
PubMed2004

Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network.

MOTIVATION: There is a general scientific need to be able to identify and evaluate what any given set of 'objects' (e.g. genes, phenotypes, chemicals, diseases) has in common. Whether it is to classify, expand upon or identify commonalities and functional groupings, informational needs can be diverse and the best source to identify relationships among a potentially heterogeneous set of objects is the scientific literature. RESULTS: We first establish a network of related objects by their co-occurrence within MEDLINE records. A set of objects within this network can then be queried to identify shared relationships, and a method is presented to score their statistical relevance by comparing observed frequencies with what would be expected in a random network model. Using Gene Ontology (GO) categories, we demonstrate that this method enables a quantitative ranking of the 'cohesiveness' of a set of objects and, importantly, allows other objects related to this set to be identified and evaluated for their 'cohesion' to it. Supplemental information: A list of ranked genes related to each GO category analyzed can be found at http://innovation.swmed.edu/IRIDESCENT/GO_relationships.htm

Abstracting and Indexing↗
PubMed2004

Knowledge discovery by automated identification and ranking of implicit relationships.

MOTIVATION: New relationships are often implicit from existing information, but the amount and growth of published literature limits the scope of analysis an individual can accomplish. Our goal was to develop and test a computational method to identify relationships within scientific reports, such that large sets of relationships between unrelated items could be sought out and statistically ranked for their potential relevance as a set. RESULTS: We first construct a network of tentative relationships between 'objects' of biomedical research interest (e.g. genes, diseases, phenotypes, chemicals) by identifying their co-occurrences within all electronically available MEDLINE records. Relationships shared by two unrelated objects are then ranked against a random network model to estimate the statistical significance of any given grouping. When compared against known relationships, we find that this ranking correlates with both the probability and frequency of object co-occurrence, demonstrating the method is well suited to discover novel relationships based upon existing shared relationships. To test this, we identified compounds whose shared relationships predicted they might affect the development and/or progression of cardiac hypertrophy. When laboratory tests were performed in a rodent model, chlorpromazine was found to reduce the progression of cardiac hypertrophy.

Abstracting and Indexing↗
PubMed2004

404 not found: the stability and persistence of URLs published in MEDLINE.

MOTIVATION: The advent of the World Wide Web has enabled unprecedented supplementation of traditional journal publications, allowing access to resources, such as video, sound, software, databases, datasets too large to publish, and even supplementary information and discussion. However, unlike traditional publications, continued availability of these online resources is not guaranteed. An automated survey was conducted to quantify the growth in Uniform Resource Locators (URLs) published to date in MEDLINE abstracts, their current availability and distribution by journal. RESULTS: Of 1630 unique URLs identified, formatting and/or spelling errors were detected within 201 (12%) of them as published. After corrections were made, a survey revealed that approximately 63% of these URLs were consistently available, and another 19% were available intermittently. The rate of failure was far worse for anonymous login to FTP sites, with only 12 of 33 sites (36%) responding. This survey also shows that journals vary disproportionately in the number of web citations published, suggesting policy implementation among a few could have a profound impact overall. Out of the 306 journals with a URL published in an abstract, Bioinformatics published the most (12% of total). AVAILABILITY: URL database and program available by request.

Abstracting and Indexing↗
PubMed2004

Simulated annealing of microarray data reduces noise and enables cross-experimental comparisons.

Microarrays are a powerful tool for assessing the genome-wide induction of a transcriptional response to internal or external stimuli, but are not considered quantitatively rigorous (i.e., the signal intensity of hybridized probe is normally used to quantify relative transcript abundance). Thus, it is difficult, if not impossible, to accurately compare separate microarray experiments without a reference standard. However, even among replicated microarray experiments, each gene varies significantly in the amount of signal detected, suggesting no single gene would be appropriate as a standard. We propose and test a method to "align" experimental transcription profiles to a set of reference experiments using simulated annealing (SA), essentially using the relative positions of all genes as a reference standard. SA attempts to find a globally optimal adjustment factor for the relative expression level of each experimental gene expression signal, given a previously observed range of gene expression measurements. By defining a relative dynamic range of gene expression under control conditions for all genes, we can more accurately compare transcription profiles between separate experiments and, potentially, between species--enabling comparative transcriptomics. Testing SA on a published dataset, we find that it significantly reduces interexperimental variation, suggesting it holds promise to accomplish this goal.

Oligonucleotide Array Sequence Analysis↗
PubMed2002

SIGNAL-Sequence Information and GeNomic AnaLysis.

An integrated software package has been developed to provide convenient graphical and textual analysis of a variety of genomic sequence features, free of charge to the biomedical research community. This package, called sequence information and genomic analysis, is available as either a stand-alone or a web-based version to enable greater versatility in access and utilization. The package can be accessed or downloaded at the following URL: http://innovation.swmed.edu/signal.htm.

Algorithms↗
PubMed2002

ARROGANT: an application to manipulate large gene collections.

ARROGANT (ARRay OrGANizing Tool) is a software tool developed to facilitate the identification, annotation and comparison of large collections of genes or clones. The objective is to enable users to compile gene/clone collections from different databases, allowing them to design experiments and analyze the collections as well as associated experimental data efficiently. ARROGANT can relate different sequence identifiers to their common reference sequence using the UniGene database, allowing for the comparison of data from two different microarray experiments. ARROGANT has been successfully used to analyze microarray expression data for colon cancer, to compile genes potentially related to cardiac diseases for subsequent resequencing (to identify single nucleotide polymorphisms, SNPs), to design a new comprehensive human cDNA microarray for cancer, to combine and compare expression data generated by different microarrays and to provide annotation for genes on custom and Affymetrix chips.

Base Sequence↗