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1.
Bioinformatics ; 18(11): 1515-22, 2002 Nov.
Article in English | MEDLINE | ID: mdl-12424124

ABSTRACT

MOTIVATION: Mining the biomedical literature for references to genes and proteins always involves a tradeoff between high precision with false negatives, and high recall with false positives. Having a reliable method for assessing the relevance of literature mining results is crucial to finding ways to balance precision and recall, and for subsequently building automated systems to analyze these results. We hypothesize that abstracts and titles that discuss the same gene or protein use similar words. To validate this hypothesis, we built a dictionary- and rule-based system to mine Medline for references to genes and proteins, and used a Bayesian metric for scoring the relevance of each reference assignment. RESULTS: We analyzed the entire set of Medline records from 1966 to late 2001, and scored each gene and protein reference using a Bayesian estimated probability (EP) based on word frequency in a training set of 137837 known assignments from 30594 articles to 36197 gene and protein symbols. Two test sets of 148 and 150 randomly chosen assignments, respectively, were hand-validated and categorized as either good or bad. The distributions of EP values, when plotted on a log-scale histogram, are shown to markedly differ between good and bad assignments. Using EP values, recall was 100% at 61% precision (EP=2 x 10(-5)), 63% at 88% precision (EP=0.008), and 10% at 100% precision (EP=0.1). These results show that Medline entries discussing the same gene or protein have similar word usage, and that our method of assessing this similarity using EP values is valid, and enables an EP cutoff value to be determined that accurately and reproducibly balances precision and recall, allowing automated analysis of literature mining results. .


Subject(s)
Bayes Theorem , Genes , Information Storage and Retrieval/methods , MEDLINE , Natural Language Processing , Proteins , Abstracting and Indexing/methods , Algorithms , Database Management Systems , Dictionaries as Topic , False Negative Reactions , False Positive Reactions , Models, Statistical , National Library of Medicine (U.S.) , Pattern Recognition, Automated , Subject Headings , United States
2.
Proc Natl Acad Sci U S A ; 99(6): 3996-4001, 2002 Mar 19.
Article in English | MEDLINE | ID: mdl-11891272

ABSTRACT

Here we provide the first evidence, to our knowledge, that estradiol (E(2)) affects learning and memory via the newly discovered estrogen receptor beta (ERbeta). In this study, ERbeta knockout (ERbetaKO) and wild-type littermates were tested for spatial learning in the Morris water maze after ovariectomy, appropriate control treatment, or one of two physiological doses of E(2). Regardless of treatment, all wild-type females displayed significant learning. However, ERbetaKOs given the low dose of E(2) were delayed in learning acquisition, and ERbetaKOs administered the higher dose of E(2) failed to learn the task. These data show that ERbeta is required for optimal spatial learning and may have implications for hormone replacement therapy in women.


Subject(s)
Estradiol/pharmacology , Gene Deletion , Learning/drug effects , Receptors, Estrogen/deficiency , Space Perception/drug effects , Animals , Behavior, Animal/drug effects , Behavior, Animal/physiology , Cues , Escape Reaction/drug effects , Escape Reaction/physiology , Estradiol/blood , Estrogen Receptor beta , Female , Hippocampus/drug effects , Hippocampus/metabolism , Learning/physiology , Maze Learning/drug effects , Maze Learning/physiology , Mice , Mice, Inbred Strains , Mice, Knockout , Organ Size/drug effects , Ovariectomy , Receptors, Estrogen/genetics , Receptors, Estrogen/metabolism , Space Perception/physiology , Uterus/drug effects , Uterus/growth & development
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