UNASSIGNED: Clinical evaluation of @entity1 and diagnosis of disorder is crucial to make decisions on appropriate therapies. In addition, in the case of @entity74 resulting from gene abnormalities, phenotypic effects may guide basic research on the mechanisms of @entity74 to find the mutated gene and therefore to propose novel targets for drug therapy. However, this approach is complicated by two facts. First, the relationship between genes and disorders is not simple: one gene may be related to @entity74 and @entity74 may be caused by mutations in different genes. Second, recognizing relevant phenotypes might be difficult for clinicians working with @entity1 of closely related @entity74 . @entity146 best illustrate these difficulties since phenotypes range from metabolic to behavioral aspects, the latter extremely complex. Based on our clinical expertise on five @entity179 , and from the wealth of bibliographical data on @entity146 , we have built a resource to infer associations between genes, chemicals, phenotypes for a total of 31 disorders. An initial step of automated text mining of the literature related to 31 disorders returned thousands of enriched terms. Fewer relevant phenotypic terms were manually selected by clinicians as relevant to the five @entity2167 of their expertise and used to analyze the complete set of disorders. Analysis of the data indicates general relationships between @entity146 , which can be used to classify and characterize them. Correlation analyses allowed us to propose novel associations of genes and drugs with disorders. More generally, the results led us to uncovering mechanisms of disease that @entity146 , for example that genes related to synaptic transmission and receptor functions tend to be involved in many disorders, whereas genes related to sensory perception and channel transport functions are associated with fewer disorders. Our study shows that starting from expertise covering a limited set of @entity16 and using text and data mining methods, meaningful and novel associations regarding genes, chemicals and phenotypes can be derived for an expanded set of @entity146 . Our results are intended for clinicians to help them evaluate @entity1 , and for basic scientists to propose new gene targets for drug therapies. This strategy can be extended to virtually all diseases and takes advantage of the ever increasing amount of biomedical literature.

Assessment of curated phenotype mining in XXXX literature.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity2167 :: ('MESH:D006311', 'Disease') :: ['neural disorders'] @entity146 :: ('MESH:D001523', 'Disease') :: ['span multiple neuropsychiatric disorders', 'neuropsychiatric disorders', 'Neuropsychiatric disorders'] @entity16 :: ('MESH:D009422', 'Disease') :: ['neurological disorders'] @entity179 :: ('MESH:D019636', 'Disease') :: ['neurodegenerative disorders'] @entity74 :: ('MESH:D030342', 'Disease') :: ['genetic disorders', 'multiple disorders', 'complex disorders', 'a disorder']
@entity146 :: (MESH:D001523,Disease) :: ['neuropsychiatric disorder']
