Berivan Baskin

Dystrophinopathy may be associated with predominantly cognitive impairment and neurobehavioral disorder, and should be considered in the differential diagnosis of unexplained cognitive or psychiatric disturbance in males.

Long‐chain 3‐hydroxyacyl‐CoA dehydrogenase deficiency is an autosomal recessive disorder affecting mitochondrial fatty acid oxidation due to mutations in the HADHA gene. We report on a 22‐month‐old child who was identified on expanded newborn screening with an abnormal acylcarnitine pattern and increased C14OH. Molecular analysis showed that the child was homozygous for the common mutation, c.1526G > C (p.Glu510Gln) in the HADHA gene. Carrier testing on the parental samples revealed that the father was heterozygous for the mutation whereas the mother did not carry the mutation. Short tandem repeat testing with markers covering both short and long arms of chromosome 2 showed that the child has paternal uniparental isodisomy. We highlight the importance of parental testing in cases of homozygosity in autosomal recessive disorders and its impact on genetic counseling of the family.

The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca . Current capabilities allow searching for human, mouse and yeast protein-interaction information.