In order to show that the newly developed K-string composition distance method, based on counting oligopeptide frequencies, for inferring phylogenetic relations of prokaryotes works equally well without requiring the ...In order to show that the newly developed K-string composition distance method, based on counting oligopeptide frequencies, for inferring phylogenetic relations of prokaryotes works equally well without requiring the whole proteome data, we used all ribosomal proteins and the set of aminoacyl tRNA synthetases for each species. The latter group has been known to yield inconsistent trees if used individually. Our trees are obtained without making any sequence alignment. Altogether 16 Archaea, 105 Bacteria and 2 Eucarya are represented on the tree. Most of the lower branchings agree well with the latest, 2003, Outline of the second edition of the Bergeys Manual of Systematic Bacteriology and the trees also suggest some relationships among higher taxa.展开更多
In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system（UMLS）concept unique identifier（CUI）. We d...In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system（UMLS）concept unique identifier（CUI）. We develop a two-step method to acquire a list of candidate CUIs and their associated preferred names using UMLS API and to choose the closest CUI by calculating the similarity between the input disorder mention and each candidate. The similarity calculation step is formulated as a classification problem and multiple features（string features,ranking features,similarity features,and contextual features） are used to normalize the disorder mentions. The results show that the multiple feature approach improves the accuracy of the normalization task from 32.99% to 67.08% compared with the Meta Map baseline.展开更多
基金This work was partly supported by the Special Funds for Major State Basic Research Projects(Grant No.G2000077308)National Natural Science Foundation of China(Grant No.30170232)+1 种基金the Innovation Project of Chinese Academy of Sciencesby a grant from Shaghai Municipality via Fudan University.
文摘In order to show that the newly developed K-string composition distance method, based on counting oligopeptide frequencies, for inferring phylogenetic relations of prokaryotes works equally well without requiring the whole proteome data, we used all ribosomal proteins and the set of aminoacyl tRNA synthetases for each species. The latter group has been known to yield inconsistent trees if used individually. Our trees are obtained without making any sequence alignment. Altogether 16 Archaea, 105 Bacteria and 2 Eucarya are represented on the tree. Most of the lower branchings agree well with the latest, 2003, Outline of the second edition of the Bergeys Manual of Systematic Bacteriology and the trees also suggest some relationships among higher taxa.
基金Supported by the National Natural Science Foundation of China(61133012,61202193,61373108)the Major Projects of the National Social Science Foundation of China(11&ZD189)+1 种基金the Chinese Postdoctoral Science Foundation(2013M540593,2014T70722)the Open Foundation of Shandong Key Laboratory of Language Resource Development and Application
文摘In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system（UMLS）concept unique identifier（CUI）. We develop a two-step method to acquire a list of candidate CUIs and their associated preferred names using UMLS API and to choose the closest CUI by calculating the similarity between the input disorder mention and each candidate. The similarity calculation step is formulated as a classification problem and multiple features（string features,ranking features,similarity features,and contextual features） are used to normalize the disorder mentions. The results show that the multiple feature approach improves the accuracy of the normalization task from 32.99% to 67.08% compared with the Meta Map baseline.
