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  • 1
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    CLUE-TIPS, Clustering Methods for Pattern Analysis of LC−MS Data (2009)
    American Chemical Society
    Details
    Publication Date: 2009-10-02
    Print ISSN: 1535-3893
    Electronic ISSN: 1535-3907
    Topics: Chemistry and Pharmacology
    Published by American Chemical Society
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  • 2
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    SeMoP: A New Computational Strategy for the Unrestricted Search for Modified Peptides Using LC−MS/MS Data (2008)
    American Chemical Society
    Details
    Publication Date: 2008-09-05
    Print ISSN: 1535-3893
    Electronic ISSN: 1535-3907
    Topics: Chemistry and Pharmacology
    Published by American Chemical Society
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  • 3
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    NetiNeti: discovery of scientific names from text using machine learning methods (2012)
    BioMed Central
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    Publication Date: 2012-08-22
    Electronic ISSN: 1471-2105
    Topics: Biology , Computer Science
    Published by BioMed Central
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  • 4
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    NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Table 1 (2012)
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    Publication Date: 2022-05-25
    Description: Table 1 summarizes the results of the comparison of the precision and recall values of Naïve Bayes and Maximum Entropy classification algorithms with various parameter estimation methods like GIS, IIS, and L-BFGS on the manually annotated American Seashell book including the Decision Tree Learning algorithm implemented in the Natural Language toolkit[http://www.nltk.org/]
    Description: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms
    Keywords: Naïve Bayes, Maximum Entropy classification, Natural Language toolkit
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 5
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    NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Table 2 (2012)
    Details
    Publication Date: 2022-05-25
    Description: A comparison of NetiNeti, TaxonFinder and FAT tool for the American Seashell Book book (http://www.biodiversitylibrary.org/item/31699) is presented in Table 2. The FAT approach has lower precision and recall values compared to NetiNeti and TaxonFinder approaches for this corpus. The names marked up by the FAT tool were compared with the manual mark up. 869 of the names identified by FAT did not match with the manually marked up set of names. Most of these unmatched names are species epithets with authorship information. We - 16 - further analyzed a random sample of 100 names out of these 869 names and examined genus information interpreted by the tool in the marked up tags. 32 of the 100 mismatched names have correctly interpreted genus names and the remaining are all true false positives with incorrect genus tags. We estimated that 278 of these 869 are correct identifications and the adjusted precision and recall values for the FAT approach were summarized in Table 2. For many of the true false positives, the FAT tool tags the species epithet, but does not seem to recognize the genus name immediately preceding the species name.
    Description: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms.
    Keywords: Precision and recall values
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 6
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    NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 2 (2011)
    Details
    Publication Date: 2022-05-25
    Description: Figure 2 summarizes the results of running the NetiNeti with Naïve Bayes algorithm on the annotated corpus (“American Seashell” book). We also compare our results with those of TaxonFinder.
    Description: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms.
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 7
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    NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 3 (2012)
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    Publication Date: 2022-05-25
    Description: The results of running NetiNeti with Naïve Bayes algorithm for classification on 136 PMC full text articles
    Description: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms.
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 8
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    NetiNeti : Discovery of Scientific Names from Text Using Machine Learning Methods Figure 1 (2011)
    Details
    Publication Date: 2022-05-26
    Description: Figure 1 demonstrates a series of training experiments with the Naïve Bayes classifier using different neighborhoods for contextual features, different sizes of positive and negative training examples and evaluated the resulting classifiers with our annotated gold standard corpus. The data sets are the results of running NetiNeti on subset of 136 PubMedCentral tagged open access articles and with no stop list.
    Description: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org we present the comparison results of various machine learning algorithms on our annotated corpus. Naïve Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms.
    Keywords: Naïve Bayes classifier, training experiments
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 9
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    NetiNeti : discovery of scientific names from text using machine learning methods (2013)
    BioMed Central
    Details
    Publication Date: 2022-05-26
    Description: © The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Bioinformatics 13 (2012): 211, doi:10.1186/1471-2105-13-211.
    Description: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9% and recall = 70.5%) compared to a popular dictionary based approach (precision = 97.5% and recall = 54.3%) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central’s full text articles annotated with scientific names, the precision and recall values are 98.5% and 96.2% respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.
    Description: This project was funded by the Ellison Medical Foundation and a grant from the National Library of Medicine (R01 LM009725).
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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