A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature.

We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available.

Cancer SIGVAR: A semiautomated interpretation tool for germline variants of hereditary cancer-related genes.

Cancer is one of the most important health issues globally and the accuracy of interpretation of cancer-related variants is critical for the clinical management of hereditary cancer. ClinGen Sequence Variant Interpretation Working Groups have developed many adaptations of American College of Medical Genetics and Genomics and the Association of Molecular Pathologists guidelines to improve the consistency of interpretation. We combined the most recent adaptations to expand the number of the criteria from 28 to 48 and developed a tool called Cancer SIGVAR to help genetic counselors interpret the clinical significance of cancer germline variants.

Functional Analysis of the Melanin-Associated Gene in .

is a sclerotial parasite, which has been investigated for commercial control of crop diseases caused by . Previously, we obtained a T-DNA insertional mutant, ZS-1TN24363, which did not produce melanin during conidiation. To understand the function of melanin in , we cloned the gene that was disrupted by the T-DNA insertion, and found that this gene, called , encoded a putative protein of 1,011 amino acids, which is a homolog of the transcription factor MR.