Biomedical subjects

Alexander Morgan

Publications and source records attributed to Alexander Morgan.

3 recordsLinked to original sources

PubMed2006

Vitelliform macular dystrophy.

PURPOSE: To investigate and integrate the photographic, angiographic, and tomographic findings from a group of patients with various stages of vitelliform macular dystrophy type 2 (VMD2; also known as Best's disease) and use this information to propose mechanisms of disease pathogenesis. DESIGN: Retrospective observational case series. PARTICIPANTS: Nine consecutive patients seen in a private practice referral setting by the authors. METHODS: Patients with VMD2 were imaged with conventional fundus and autofluorescence photography, fluorescein angiography, fundus photography, and optical coherence tomography (OCT). MAIN OUTCOME MEASURES: The integrated ocular imaging findings. RESULTS: Early stage lesions were smaller and had accumulation of yellowish material in the central macula. This material was highly autofluorescent and appeared to be located on the outer retinal surface by OCT. Later stages were characterized by larger lesions with central clearing of the yellowish material and deposition of autofluorescent subretinal material at the outer borders of the lesion. Both early and late lesions had a subretinal fluid component with no reflectivity as detected by OCT. Fluorescein angiography showed transmission defects with a suggestion of late leakage, much like that seen in chronic central serous chorioretinopathy (CSC). CONCLUSIONS: Similar to that seen in CSC, patients with VMD2 have an accumulation of material on the outer retina, which may represent shed photoreceptor outer segments in association with subretinal fluid.

Adolescent↗

PubMed2005

Overview of BioCreAtIvE task 1B: normalized gene lists.

BACKGROUND: Our goal in BioCreAtIve has been to assess the state of the art in text mining, with emphasis on applications that reflect real biological applications, e.g., the curation process for model organism databases. This paper summarizes the BioCreAtIvE task 1B, the "Normalized Gene List" task, which was inspired by the gene list supplied for each curated paper in a model organism database. The task was to produce the correct list of unique gene identifiers for the genes and gene products mentioned in sets of abstracts from three model organisms (Yeast, Fly, and Mouse). RESULTS: Eight groups fielded systems for three data sets (Yeast, Fly, and Mouse). For Yeast, the top scoring system (out of 15) achieved 0.92 F-measure (harmonic mean of precision and recall); for Mouse and Fly, the task was more difficult, due to larger numbers of genes, more ambiguity in the gene naming conventions (particularly for Fly), and complex gene names (for Mouse). For Fly, the top F-measure was 0.82 out of 11 systems and for Mouse, it was 0.79 out of 16 systems. CONCLUSION: This assessment demonstrates that multiple groups were able to perform a real biological task across a range of organisms. The performance was dependent on the organism, and specifically on the naming conventions associated with each organism. These results hold out promise that the technology can provide partial automation of the curation process in the near future.

Animals↗

PubMed2005

BioCreAtIvE task 1A: gene mention finding evaluation.

BACKGROUND: The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. "Finding mentions" is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI). RESULTS: 15 teams took part in task 1A. A number of teams achieved scores over 80% F-measure (balanced precision and recall). The teams that tried to use their task 1A systems to help on other BioCreAtIvE tasks reported mixed results. CONCLUSION: The 80% plus F-measure results are good, but still somewhat lag the best scores achieved in some other domains such as newswire, due in part to the complexity and length of gene names, compared to person or organization names in newswire.

Computational Biology↗