Juan Antonio Lossio-Ventura, C. Jonquet, M. Roche, M. Teisseire
{"title":"Towards a Mixed Approach to Extract Biomedical Terms from Text Corpus","authors":"Juan Antonio Lossio-Ventura, C. Jonquet, M. Roche, M. Teisseire","doi":"10.4018/IJKDB.2014010101","DOIUrl":"https://doi.org/10.4018/IJKDB.2014010101","url":null,"abstract":"The objective of this paper is to present a methodology to extract and rank automatically biomedical terms from free text. The authors present new extraction methods taking into account linguistic patterns specialized for the biomedical domain, statistic term extraction measures such as C-value and statistic keyword extraction measures such as Okapi BM25, and TFIDF. These measures are combined in order to improve the extraction process and the authors investigate which combinations are the more relevant associated to different contexts. Experimental results show that an appropriate harmonic mean of C-value associated to keyword extraction measures offers better precision, both for single-word and multi-words term extraction. Experiments describe the extraction of English and French biomedical terms from a corpus of laboratory tests available online. The results are validated by using UMLS in English and only MeSH in French as reference dictionary.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128977920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Srinivasan, G. Agarwal, R. Bhaskara, R. Gadkari, O. Krishnadev, B. Lakshmi, Swapnil Mahajan, S. Mohanty, R. Mudgal, R. Rakshambikai, S. Sandhya, Govindarajan Sudha, L. S. Swapna, Nidhi Tyagi
{"title":"Influence of Genomic and Other Biological Data Sets in the Understanding of Protein Structures, Functions and Interactions","authors":"N. Srinivasan, G. Agarwal, R. Bhaskara, R. Gadkari, O. Krishnadev, B. Lakshmi, Swapnil Mahajan, S. Mohanty, R. Mudgal, R. Rakshambikai, S. Sandhya, Govindarajan Sudha, L. S. Swapna, Nidhi Tyagi","doi":"10.4018/jkdb.2011010102","DOIUrl":"https://doi.org/10.4018/jkdb.2011010102","url":null,"abstract":"In the post-genomic era, biological databases are growing at a tremendous rate. Despite rapid accumulation of biological information, functions and other biological properties of many putative gene products of various organisms remain either unknown or obscure. This paper examines how strategic integration of large biological databases and combinations of various biological information helps address some of the fundamental questions on protein structure, function and interactions. New developments in function recognition by remote homology detection and strategic use of sequence databases aid recognition of functions of newly discovered proteins. Knowledge of 3-D structures and combined use of sequences and 3-D structures of homologous protein domains expands the ability of remote homology detection enormously. The authors also demonstrate how combined consideration of functions of individual domains of multi-domain proteins helps in recognizing gross biological attributes. This paper also discusses a few cases of combining disparate biological datasets or combination of disparate biological information in obtaining new insights about protein-protein interactions across a host and a pathogen. Finally, the authors discuss how combinations of low resolution structural data, obtained using cryoEM studies, of gigantic multi-component assemblies, and atomic level 3-D structures of the components is effective in inferring finer features in the assembly.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129791732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble","authors":"S. Dash, B. Patra","doi":"10.4018/IJKDB.2016010101","DOIUrl":"https://doi.org/10.4018/IJKDB.2016010101","url":null,"abstract":"High dimension and small sample size is an inherent problem of gene expression datasets which makes the analysis process more complex. The present study has developed a novel learning scheme that encapsulates a hybrid evolutionary fuzzy-rough feature selection model with an adaptive neural net ensemble. Fuzzy-rough method deals with uncertainty and impreciseness of real valued gene expression dataset and evolutionary search concept optimizes the subset selection process. The efficiency of the hybrid-FRGSNN model is evaluated by the proposed neural net ensemble learning algorithm. Again to prove the learning capability of ensemble algorithm, performance of the component classifiers pairing with FR, GSNN and FRGSNN are compared with proposed hybrid-FRGSNN based ensemble model. In addition to this, efficiency of neural net ensemble is compared with two classical and one advanced ensemble learning algorithms.","PeriodicalId":160270,"journal":{"name":"Int. J. Knowl. Discov. Bioinform.","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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