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503 results on '"Chen, Hsin-Hsi"'

153. List of contributors

Author

Azhie, Amirhossein, Bhat, Mamatha, Calderaro, Julien, Chan, Anthony Wing-Hung, Chang, Edward Y., Chaudhary, Kumardeep, Chen, Yen-Yuan, Chen, Hsin-Hsi, Chu, Alicia, Chung, Raymond T., Kao, Jia-Horng, Kim, Kyung Won, Kusko, Rebecca, Lee, Seung Soo, Leung, Howard Ho-Wai, Li, Ting, Li, Yu-Chuan, Liu, Zhichao, Ma, Andy Jinhua, Malhi, Harsheen Kaur, Mathews, Liza Rachel, Park, Hyo Jung, Przybyszewski, Eric M., Rabindranath, Madhumitha, Rajan, Rawat, Akanksha, Sato, Masaya, Simon, Tracey G., Su, Tung-Hung, Thakkar, Shraddha, Tong, Weida, Vij, Mukul, Wong, Grace Lai-Hung, Wong, Vincent Wai-Sun, Wu, Cheng-Kuang, Yang, Hsuan-Chia, Yang, Hwai-I, Yen, An-Zi, Yu, Kun-Hsing, and Yuen, Pong-Chi

Published
2023
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178. TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining

Author

Chen Hsin-Hsi, Huang Hsuan-Cheng, Fang Yu-Ching, and Juan Hsueh-Fen

Subjects
Other systems of medicine, RZ201-999
Abstract

Abstract Background Traditional Chinese Medicine (TCM), a complementary and alternative medical system in Western countries, has been used to treat various diseases over thousands of years in East Asian countries. In recent years, many herbal medicines were found to exhibit a variety of effects through regulating a wide range of gene expressions or protein activities. As available TCM data continue to accumulate rapidly, an urgent need for exploring these resources systematically is imperative, so as to effectively utilize the large volume of literature. Methods TCM, gene, disease, biological pathway and protein-protein interaction information were collected from public databases. For association discovery, the TCM names, gene names, disease names, TCM ingredients and effects were used to annotate the literature corpus obtained from PubMed. The concept to mine entity associations was based on hypothesis testing and collocation analysis. The annotated corpus was processed with natural language processing tools and rule-based approaches were applied to the sentences for extracting the relations between TCM effecters and effects. Results We developed a database, TCMGeneDIT, to provide association information about TCMs, genes, diseases, TCM effects and TCM ingredients mined from vast amount of biomedical literature. Integrated protein-protein interaction and biological pathways information are also available for exploring the regulations of genes associated with TCM curative effects. In addition, the transitive relationships among genes, TCMs and diseases could be inferred through the shared intermediates. Furthermore, TCMGeneDIT is useful in understanding the possible therapeutic mechanisms of TCMs via gene regulations and deducing synergistic or antagonistic contributions of the prescription components to the overall therapeutic effects. The database is now available at http://tcm.lifescience.ntu.edu.tw/. Conclusion TCMGeneDIT is a unique database that offers diverse association information on TCMs. This database integrates TCMs with biomedical studies that would facilitate clinical research and elucidate the possible therapeutic mechanisms of TCMs and gene regulations.

Published
2008
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181. Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement.

Author

Lee, Yang‐Yin, Ke, Hao, Yen, Ting‐Yu, Huang, Hen‐Hsen, and Chen, Hsin‐Hsi

Subjects
ALGORITHMS, INFORMATION retrieval, LEARNING, LINGUISTICS, MEMORY, NATURAL language processing, SEMANTICS
Abstract

In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state‐of‐the‐art approaches. The experimental results show that our method can outperform state‐of‐the‐art approaches in all the selected English benchmark data sets. [ABSTRACT FROM AUTHOR]

Published
2020
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188. That Makes Sense

Author

Yen, Ting-Yu, primary, Lee, Yang-Yin, additional, Huang, Hen-Hsen, additional, and Chen, Hsin-Hsi, additional

Published
2018
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198. Mining opinions from the Web: beyond relevance retrieval

Author

Ku, Lun-Wei and Chen, Hsin-Hsi

Subjects
Data mining -- Methods, Electronic news gathering -- Information management, Data warehousing/data mining, Company systems management, Computers, Library and information science, Mathematics
Abstract

An opinion mining system with algorithms for opinion extraction, summarization, and tracking is proposed. Machine learning methods are shown to be unsuitable for word-level sentiment mining, but opinion extraction is enhanced with the use of topical words.

Published
2007

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