Biomedical subjects

Ray Dingledine

Publications and source records attributed to Ray Dingledine.

4 recordsLinked to original sources

PubMed2004

Comparison of two schemes for automatic keyword extraction from MEDLINE for functional gene clustering.

One of the key challenges of microarray studies is to derive biological insights from the unprecedented quatities of data on gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the nature of the functional links among genes within the derived clusters. However, the quality of the keyword lists extracted from biomedical literature for each gene significantly affects the clustering results. We extracted keywords from MEDLINE that describes the most prominent functions of the genes, and used the resulting weights of the keywords as feature vectors for gene clustering. By analyzing the resulting cluster quality, we compared two keyword weighting schemes: normalized z-score and term frequency-inverse document frequency (TFIDF). The best combination of background comparison set, stop list and stemming algorithm was selected based on precision and recall metrics. In a test set of four known gene groups, a hierarchical algorithm correctly assigned 25 of 26 genes to the appropriate clusters based on keywords extracted by the TDFIDF weighting scheme, but only 23 og 26 with the z-score method. To evaluate the effectiveness of the weighting schemes for keyword extraction for gene clusters from microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle were used as a second test set. Using established measures of cluster quality, the results produced from TFIDF-weighted keywords had higher purity, lower entropy, and higher mutual information than those produced from normalized z-score weighted keywords. The optimized algorithms should be useful for sorting genes from microarray lists into functionally discrete clusters.

Artificial Intelligence↗

PubMed2004

Text mining functional keywords associated with genes.

Modern experimental techniques provide the ability to gather vast amounts of biological data in a single experiment (e.g. DNA microarray experiment), making it extremely difficult for the researcher to interpret the data and form conclusions about the functions of the genes. Current approaches provide useful information that organizes or relates genes, but a major shortcoming is they either do not address specific functions of the genes or are constrained by functions predefined in other databases, which can be biased, incomplete, or out-of-date. We extended Andrade and Valencia's method [1] to statistically mine functional keywords associated with genes from MEDLINE abstracts. The MEDLINE abstracts are analyzed statistically to score and rank keywords for each gene using a background set of words for baseline frequencies. We generally got very good functional keyword information about the genes we tested, which was confirmed by searching for the individual keywords in context. The keywords extracted by our algorithm reveal a wealth of potential functional concepts, which were not represented in existing public databases. We feel that this approach is general enough to apply to medical and biological literature to find other relationships: drugs vs. genes, risk-factors vs. genes, etc.

Algorithms↗

PubMed2002

Altered histone acetylation at glutamate receptor 2 and brain-derived neurotrophic factor genes is an early event triggered by status epilepticus.

The mechanisms underlying seizure-induced changes in gene expression are unclear. Using a chromatin immunoprecipitation assay, we found that acetylation of histone H4 in rat hippocampal CA3 neurons was reduced at the glutamate receptor 2 (GluR2; GRIA2) glutamate receptor promoter but increased at brain-derived neurotrophic factor promoter P2 as soon as 3 hr after induction of status epilepticus by pilocarpine. This result indicates that status epilepticus rapidly activates different signal pathways to modulate histone acetylation in a promoter-specific manner. H4 deacetylation preceded seizure-induced GluR2 mRNA downregulation. The histone deacetylase inhibitor trichostatin A prevented and quickly reversed deacetylation of GluR2-associated histones. Trichostatin A also blunted seizure-induced downregulation of GluR2 mRNA in CA3. Thus, rapid gene-specific changes in histone acetylation patterns may be a key early step in the pathological processes triggered by status epilepticus.

Acetylation↗

PubMedDate not supplied

Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms.

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.

Abstracting and Indexing↗