• Journal of the Korean Society for Library and Information Science
• /
• v.50 no.2
• /
• pp.309-336
• /
• 2016

This paper introduces a relation extraction system that can be used in identifying and classifying semantic relations between biomedical entities in scientific texts using machine learning methods such as Support Vector Machines (SVM). The suggested system includes many useful functions capable of extracting various linguistic features from sentences having a pair of biomedical entities and applying them into training relation extraction models for maximizing their performance. Three globally representative collections in biomedical domains were used in the experiments which demonstrate its superiority in various biomedical domains. As a result, it is most likely that the intensive experimental study conducted in this paper will provide meaningful foundations for research on bio-text analysis based on machine learning.

• Journal of Life Science
• /
• v.33 no.8
• /
• pp.663-679
• /
• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.26 no.4
• /
• pp.551-556
• /
• 2012

The database for the oriental medicine had been existed in documentation in past times and it has been developed to the database type for random accesses in the information society. However, the aspects of the database are not so diversified and the database for the bio herbal material exists in widened type dictionary style. It is a situation that the database which handles the in-depth raw herbal medicines is not sufficient in its quantity and quality. Korean wild grape is a deciduous plant categorized into the Vitaceae and it was found experimentally that it has various medical effects. It is one of the medical materials with higher potentiality of academic study and commercialization recently because it has a bigger possibility to be applied into diverse industrial fields including the medical product for health, food and beauty. We constituted the cooperative system among the Muju cluster business group for Korean mountain wild grapes, Physiology Laboratory in Kyung Hee University Oriental Medicine and Medical Classics Laboratory in Kyung Hee University Oriental Medicine with a view to focusing on such potentiality and a database for Korean wild grapes was made a touchstone for establishing the in-depth database for the single bio medical materials. First of all, the literatures based on the North East Asia in ancient times had been categorized into the classical literature (Korean literature published by government organization, Korean classical literature, Chinese classical literature and classical literature fro Korean and Chinese oriental medicine) and modern literature (Modern literature for oriental medicine, modern literature for domestic and foreign herbal medicine) to cover the eastern and western research records and writings related to Korean wild grapes and the text-mining work has been performed through the cooperation system with the Medical Classics Laboratory in Kyung Hee University Oriental Medicine. First of all, the data for the experiment and theory for Korean wild grape were collected for the Medline database controlled by the Parliament Library of USA to arrange the domestic and foreign theses with topic for Korean wild grapes and the network hyperlink function and down load function were mounted for self-thesis searching function and active view based on the collected data. The thesis searching function provides various auxiliary functions and the searching is available according to the diverse searching/queries such as the name of sub species of Korean wild grape, the logical intersection index for the active ingredients, efficacy and elements. It was constituted for the researchers who design the Korean wild grape study to design of easier experiment. In addition, the data related to the patents for Korean wild grape which were collected from European Patent Office in response to the commercialization possibility and the system available for searching and view was established in the same viewpoint. Perl was used for the query programming and MS-SQL for database establishment and management in the designing of this database. Currently, the data is available for free use and the address is as follows. http://163.180.41.43:8011/index.html

• Journal of the Korean Society for Library and Information Science
• /
• v.51 no.4
• /
• pp.227-248
• /
• 2017

This paper introduces an integrated model for systematically constructing a linguistic resource database that can be used by machine learning-based biomedical information extraction systems. The proposed method suggests an orderly process of collecting and constructing dictionaries and training sets for both named-entity recognition and relation extraction. Multiple heterogeneous structures for the resources which are collected from diverse sources are analyzed to derive essential items and fields for constructing the integrated database. All the collected resources are converted and refined to build an integrated linguistic resource storage. In this paper, we constructed entity dictionaries of gene, protein, disease and drug, which are considered core linguistic elements or core named entities in the biomedical domains and conducted verification tests to measure their acceptability.

• Journal of KIISE
• /
• v.41 no.9
• /
• pp.652-665
• /
• 2014

Many important terminologies in biomedical text are expressed as abbreviations or acronyms. We newly suggest a semantic link topic model based on the concepts of topic and dependency link to disambiguate biomedical abbreviations and cluster long form variants of abbreviations which refer to the same senses. This model is a generative model inspired by the latent Dirichlet allocation (LDA) topic model, in which each document is viewed as a mixture of topics, with each topic characterized by a distribution over words. Thus, words of a document are generated from a hidden topic structure of a document and the topic structure is inferred from observable word sequences of document collections. In this study, we allow two distinct word generation to incorporate semantic dependencies between words, particularly between expansions (long forms) of abbreviations and their sentential co-occurring words. Besides topic information, the semantic dependency between words is defined as a link and a new random parameter for the link presence is assigned to each word. As a result, the most probable expansions with respect to abbreviations of a given abstract are decided by word-topic distribution, document-topic distribution, and word-link distribution estimated from document collection though the semantic dependency link topic model. The abstracts retrieved from the MEDLINE Entrez interface by the query relating 22 abbreviations and their 186 expansions were used as a data set. The link topic model correctly predicted expansions of abbreviations with the accuracy of 98.30%.