Text mining and pattern clustering for relation extraction of breast cancer and related genes

Kawashima, Koya; Bai, Wenjun; Quan, Changqin

doi:10.1109/snpd.2017.8022701

Cited by 8 publications

(3 citation statements)

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“…Clustering-based model. Kawashima et al (2017) propose the method for RE of breast cancer and related genes using text mining and pattern clustering and propose a two-step method that involves text mining and pattern clustering. Text mining is first used to identify the relevant sentences from the literature, and the clustering algorithm is then used to group the sentences based on their syntactic and semantic similarities.…”

Section: Unsupervised Methodsmentioning

confidence: 99%

Models and Techniques for Domain Relation Extraction: A Survey

JiaHui

Yue

Duan

2023

JDSIS

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As the significant sub-task of information extraction, relation extraction aims to identify and classify semantic relations between pairs of entities and is widely adopted as the foundation of downstream applications including knowledge graphs, intelligent question answering, text mining and sentiment analysis. Different from general knowledge, domain knowledge is pertinent to specific fields which include a wealth of proprietary entities and relations. Besides, most of the data are formed as documents rather than sentences. In this paper, the task of domain relation extraction is defined, and the common domains are presented. Furthermore, we provide a systematic review of state-of-the-art techniques as well as the latest trends. We survey different neural network based techniques for relation extraction and describe the overall framework, training procedures, as well as the pros and cons of these techniques. Then, we introduce and compare the corpus and metrics used for domain relation extraction tasks. Finally, we conclude and discuss future research issues of domain relation extraction.

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Section: Unsupervised Methodsmentioning

confidence: 99%

Models and Techniques for Domain Relation Extraction: A Survey

JiaHui

Yue

Duan

2023

JDSIS

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“…An experimental approach of unsupervised learning, combining text mining and pattern mining techniques, was used for relation extraction for breast cancer and affiliated genes in the work of Kawashima et al [57]. They extracted the related genes from PubMed articles and used them as data in vectors for clustering analysis and joined them with a list of breast cancer related genes.…”

Section: Cancer and Text Miningmentioning

confidence: 99%

Text Mining for Building Biomedical Networks Using Cancer as a Case Study

Conceição

Couto

2021

Biomolecules

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In the assembly of biological networks it is important to provide reliable interactions in an effort to have the most possible accurate representation of real-life systems. Commonly, the data used to build a network comes from diverse high-throughput essays, however most of the interaction data is available through scientific literature. This has become a challenge with the notable increase in scientific literature being published, as it is hard for human curators to track all recent discoveries without using efficient tools to help them identify these interactions in an automatic way. This can be surpassed by using text mining approaches which are capable of extracting knowledge from scientific documents. One of the most important tasks in text mining for biological network building is relation extraction, which identifies relations between the entities of interest. Many interaction databases already use text mining systems, and the development of these tools will lead to more reliable networks, as well as the possibility to personalize the networks by selecting the desired relations. This review will focus on different approaches of automatic information extraction from biomedical text that can be used to enhance existing networks or create new ones, such as deep learning state-of-the-art approaches, focusing on cancer disease as a case-study.

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“…For example, ATF3 is found as an oncogene in breast cancer, but it is a tumor suppressor in prostate cancer. For individual cancer phenotypes, text-mining has been applied to extract the relationship between breast cancer and candidate genes, and candidate association words were found to point to the relationship between breast cancer and related genes using pattern clustering (27). In another example, clinical concepts and genes associated with colorectal cancer were explored through literature and statistical analysis of clinical information and candidates (28).…”

Section: Editorial Commentarymentioning

confidence: 99%