PubMed Health⌕ Search

Biomedical subjects

Adi L Tarca

Publications and source records attributed to Adi L Tarca.

3 recordsLinked to original sources
PubMed2006

New Onto-Tools: Promoter-Express, nsSNPCounter and Onto-Translate.

The Onto-Tools suite is composed of an annotation database and eight complementary, web-accessible data mining tools: Onto-Express, Onto-Compare, Onto-Design, Onto-Translate, Onto-Miner, Pathway-Express, Promoter-Express and nsSNPCounter. Promoter-Express is a new tool added to the Onto-Tools ensemble that facilitates the identification of transcription factor binding sites active in specific conditions. nsSNPCounter is another new tool that allows computation and analysis of synonymous and non-synonymous codon substitutions for studying evolutionary rates of protein coding genes. Onto-Translate has also been enhanced to expand its scope and accuracy by fully utilizing the capabilities of the Onto-Tools database. Currently, Onto-Translate allows arbitrary mappings between 28 types of IDs for 53 organisms. Onto-Tools are freely available at http://vortex.cs.wayne.edu/Projects.html.

Binding Sites↗
PubMed2006

Analysis of microarray experiments of gene expression profiling.

The study of gene expression profiling of cells and tissue has become a major tool for discovery in medicine. Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of disease in obstetrics and gynecology. Moreover, an unbiased and systematic study of gene expression profiling should allow the establishment of a new taxonomy of disease for obstetric and gynecologic syndromes. Thus, a new era is emerging in which reproductive processes and disorders could be characterized using molecular tools and fingerprinting. The design, analysis, and interpretation of microarray experiments require specialized knowledge that is not part of the standard curriculum of our discipline. This article describes the types of studies that can be conducted with microarray experiments (class comparison, class prediction, class discovery). We discuss key issues pertaining to experimental design, data preprocessing, and gene selection methods. Common types of data representation are illustrated. Potential pitfalls in the interpretation of microarray experiments, as well as the strengths and limitations of this technology, are highlighted. This article is intended to assist clinicians in appraising the quality of the scientific evidence now reported in the obstetric and gynecologic literature.

Algorithms↗