Investigation into biomedical literature classification using support vector machines

Polavarapu, Nalini; Navathe, Shamkant B.; Ramnarayanan, R.; Haque, Anamul; Sahay, Sanjay K.; Liu, Y.

doi:10.1109/csb.2005.36

Cited by 21 publications

(12 citation statements)

References 12 publications

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“…SVM classifiers have been applied previously in various domains from bioinformatics to web mining. They often outperform other methods, showing especially promising results for text‐categorization tasks in the biomedical domain 18,19 …”

Section: Methodsmentioning

confidence: 99%

Creating Reference Datasets for Systems Biology Applications Using Text Mining

Krallinger

Rojas

Valencia

2009

Annals of the New York Academy of Sciences

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High-throughput experimental techniques are generating large data collections with the aim of identifying novel entities involved in fundamental cellular processes as well as drawing a systematic picture of the relationships between individual components. Determining the accuracy of the resulting data and the selection of a subset of targets for more careful characterizations often requires relying on information provided by manually annotated data repositories. These repositories are incomplete and cover only a small fraction of the knowledge contained in the literature. We propose in this paper the use of text-mining technologies to extract, organize, and present information relevant for a particular biological topic. The aims of the resulting approach are (1) to enable topic-centric biological literature navigation, (2) to assist in the construction of manually revised data repositories, (3) to provide prioritization of biological entities for experimental studies, and (4) to enable human interpretation of large-scale experiments by providing direct links of bio-entities to relevant descriptions in the literature.

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Section: Methodsmentioning

confidence: 99%

Creating Reference Datasets for Systems Biology Applications Using Text Mining

Krallinger

Rojas

Valencia

2009

Annals of the New York Academy of Sciences

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“…The IAS was treated as a standard document classification problem [31,32], where abstracts were classified as curatable if they contained curatable protein interaction information and noncuratable otherwise. Document classification techniques typically use a bag-of-words approach, which ignores the word order in the document.…”

Section: Methodsmentioning

confidence: 99%

Automating curation using a natural language processing pipeline

et al. 2008

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Background: The tasks in BioCreative II were designed to approximate some of the laborious work involved in curating biomedical research papers. The approach to these tasks taken by the University of Edinburgh team was to adapt and extend the existing natural language processing (NLP) system that we have developed as part of a commercial curation assistant. Although this paper concentrates on using NLP to assist with curation, the system can be equally employed to extract types of information from the literature that is immediately relevant to biologists in general.

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“…The SVM model has been used for many biomedical tasks, such as microarray data analysis [ 50 ] , classifi cation [ 51 ] , information extraction [ 52 ] , and image segmentation [ 53 ] . SVM model can leverage an arbitrary set of features to produce accurate and robust results on a sound theoretical basis, with powerful generalization ability due to optimizing margins.…”

Section: Discriminative Modelmentioning

confidence: 99%

Natural Language Processing, Electronic Health Records, and Clinical Research

Liu

Weng

2012

Health Informatics

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Electronic health records (EHR) capture "real-world" disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical natural language processing and its common uses for extracting and transforming narrative information in EHR to support clinical research. Keywords Electronic health records • Biomedical natural language processing • Sublanguage approach • Machine-learning approach • Decision tree • Rule-based approachElectronic health records (EHR) capture "real-world" disease and care processes and hence offer richer and more generalizable data for comparative effectiveness research [ 1 ] than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research [ 2 ] . A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical language processing and its common uses for extracting and transforming narrative information in EHR to support clinical research.

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Investigation into biomedical literature classification using support vector machines

Cited by 21 publications

References 12 publications

Creating Reference Datasets for Systems Biology Applications Using Text Mining

Creating Reference Datasets for Systems Biology Applications Using Text Mining

Automating curation using a natural language processing pipeline

Natural Language Processing, Electronic Health Records, and Clinical Research

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