• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.109-136
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• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• Journal of Marine Bioscience and Biotechnology
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• v.12 no.1
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• pp.1-10
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• 2020

Asthma is a complex inflammatory disease of the lung characterized by variable airflow obstruction, airway hyperresponsiveness, airway inflammation, and reduction of respiratory function. Its prevalence and incidence are increasing because of the effect of various environmental and lifestyle risk factors. Steroid inhalation, long-acting agonists, and other synthetic drugs are used for the treatment of this disease. However, they have some side effects and show unsatisfied result and response after treatment. Therefore, many researchers have focused on the development of natural product-related treatment for asthma to suppress the side effects and unsatisfied results. Seaweeds contain various bioactive compounds with anti-inflammatory, antibacterial, and anti-oxidant activities. Thus, we investigated the asthma treatment-related literature using marine algae via the Google scholar search engine. Consequently, the literature is rarely investigated, but is increasing steadily. The literature was performed as a comparison study with an ovalbumin-induced group or drug-treated group, and investigated the antiasthma activity of algae ethanol extract. Although many researchers have studied marine algae-derived therapeutic agents for asthma, the amount of literature is rare compared with those of herbal medicine-derived therapeutic agents. Conclusively, we suggest that many researchers should investigate and develop algae-derived therapeutic agents for asthma treatment.

• ETRI Journal
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• v.37 no.4
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• pp.813-823
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• 2015

Because protein is a primary element responsible for biological or biochemical roles in living bodies, protein function is the core and basis information for biomedical studies. However, recent advances in bio technologies have created an explosive increase in the amount of published literature; therefore, biomedical researchers have a hard time finding needed protein function information. In this paper, a classification system for biomedical literature providing protein function evidence is proposed. Note that, despite our best efforts, we have been unable to find previous studies on the proposed issue. To classify papers based on protein function evidence, we should consider whether the main claim of a paper is to assert a protein function. We, therefore, propose two novel features - protein and assertion. Our experimental results show a classification performance with 71.89% precision, 90.0% recall, and a 79.94% F-measure. In addition, to verify the usefulness of the proposed classification system, two case study applications are investigated - information retrieval for protein function and automatic summarization for protein function text. It is shown that the proposed classification system can be successfully applied to these applications.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.51-60
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• 2006

The paradigm in biology is currently changing from that of conducting hypothesis-driven individual experiments to that of utilizing the results of a massive data analysis with appropriate computational tools. We present LayMap, an implemented visualization system that helps the user to deal with a high volume of the biomedical literature such as MEDLINE, through the layered maps that are constructed on the results of an information extraction system. LayMap also utilizes filtering and granularity for an enhanced view of the results. Since a biomedical information extraction system gives rise to a focused and effective way of slicing up the data space, the combined use of LayMap with such an information extraction system can help the user to navigate the data space in a speedy and guided manner. As a case study, we have applied the system to datasets of journal abstracts on 'MAPK pathway' and 'bufalin' from MEDLINE. With the proposed visualization, we have successfully rediscovered pathway maps of a reasonable quality for ERK, p38 and JNK. Furthermore, with respect to bufalin, we were able to identify the potentially interesting relation between the Chinese medicine Chan su and apoptosis with a high level of detail.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.111-120
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• 2015

Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

• Journal of the Korean Society for information Management
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• v.31 no.1
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• pp.231-250
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• 2014

Due to the recent development of Information and Communication Technologies (ICT), the amount of research publications has increased exponentially. In response to this rapid growth, the demand of automated text processing methods has risen to deal with massive amount of text data. Biomedical text mining discovering hidden biological meanings and treatments from biomedical literatures becomes a pivotal methodology and it helps medical disciplines reduce the time and cost. Many researchers have conducted literature-based discovery studies to generate new hypotheses. However, existing approaches either require intensive manual process of during the procedures or a semi-automatic procedure to find and select biomedical entities. In addition, they had limitations of showing one dimension that is, the cause-and-effect relationship between two concepts. Thus;this study proposed a novel approach to discover various relationships among source and target concepts and their intermediate concepts by expanding intermediate concepts to multi-levels. This study provided distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.

• Journal of Marine Bioscience and Biotechnology
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• v.11 no.1
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• pp.1-13
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• 2019

Seaweeds contain various bioactive compounds. Especially, brown algae (Phaeophyceae), the second abundant group of seaweeds, contain numerous nutraceutical and pharmaceutical substances. In this review, we investigated on the brown algae-related patents and literature. Consequently, the research and development (R&D) trends of patent related to brown algae showed that the large majority was applied as the composition of stem cell culture medium and mostly used as active substances. In conclusion, we suggested that many researchers try to investigate and develop applications of brown algae as the sophisticated-level biomedical materials because brown algae are actively developing as simple-level biomedical materials.

• Clinical Endoscopy
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• v.56 no.1
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• pp.119-124
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• 2023

Inflammatory pseudotumor (IPT) is a rare benign tumor of unknown etiology that can occur in almost any organ system. It has neoplastic features such as local recurrence, invasive growth, and vascular invasion, leading to the possibility of malignant sarcomatous changes. The clinical presentations of colonic IPT may include abdominal pain, anemia, a palpable mass, and intestinal obstruction. A few cases of colonic IPT have been reported, but colonic IPT with pedunculated morphology is very rare. Furthermore, since it can mimic malignant polyps, understanding the endoscopic findings of colonic IPT is important for proper treatment. Herein, we present a case of colonic IPT with pseudosarcomatous changes, presenting as a large polyp, mimicking a malignant polyp in the cecum, along with a literature review.

• Genomics & Informatics
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• v.2 no.2
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• pp.99-106
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• 2004

In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein­protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S. cerevisiae (bakers yeast) from MIPS and SGD.

• Bioinformatics and Biosystems
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• v.2 no.1
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• pp.33-36
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• 2007

Scientific literature is the most reliable and comprehensive source of knowledge for scientific and biomedical information. Citation information in the literature is also reliable source for linking between literatures. We proposed CiNet, a graphic user interface based tool that extracts the trend of the research using citation information. We can navigate related literatures and extract keywords from the linked literature using this tool. These extracted keywords will be helpful to researchers who want to survey the information.