Publications by authors named "Raphael Gonen"

Existing algorithms for automated segmentation of chromosomes and centromeres do not work well for condensed, C-banded and DAPI-stained chromosomes and centromeres. Overlapping and aggregation, which frequently occur in metaphase spreads, introduce additional challenges to the counting of chromosomes and centromeres in the Dicentrics Chromosome Assay (DCA). In this paper, we introduce adaptive algorithms, for segmentation of difficult metaphase spreads that include overlapping and aggregated chromosomes.

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The dicentric chromosome assay (DCA), is considered the 'gold standard' for radiation biodosimetry. Yet, DCA, as currently implemented, may be impractical for emergency response applications, especially when time is of the essence, owing to its labor-intensive and time-consuming nature. The growth of a primary lymphocyte culture for 48 h in vitro is required for DCA, and manual scoring of dicentric chromosomes (DCs) requires an additional 24-48 h, resulting in an overall processing time of 72-96 h for dose estimation.

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Many chromosome assays rely on the quantification of chromosome abnormalities in cells, and one important abnormality is the existence of more than one centromere for each chromosome. The quantification of such abnormalities has been studied before. However, this process is labor-intensive and time consuming.

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Background: The prevalence and the role of AGG interruptions within the FMR1 gene in the normal population is unknown. In this study, we investigated the frequent of AGG loss, in one or two alleles within the normal population. The role of AGG in the FMR1 stability has been assessed by correlating AGG loss to the prevalence of premutation/full mutation in two ethnic groups differing in their consanguinity rate: high versus low consanguinity rate (HCR vs.

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The main focus of this pilot study is to develop a statistical approach that is suitable to model data obtained by different detection methods. The methods used in this study examine the possibility to detect early breast cancer (BC) by exhaled breath and urine samples analysis. Exhaled breath samples were collected from 48 breast cancer patients and 45 healthy women that served as a control group.

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