KGHC: a knowledge graph for hepatocellular carcinoma

Li, Nan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

doi:10.1186/s12911-020-1112-5

Cited by 28 publications

(27 citation statements)

References 34 publications

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“…However, for the sake of precision medicine on some specific human diseases or health conditions, there is the need for very fine-grained knowledge with a specific focus on them. In this context, COVID-KG [9] included biomedical knowledge with a specific focus on COVID-19; KGHC [10] is a knowledge graph constructed focusing on addressing hepatocellular carcinoma. Following this idea, to adapt our KG to addressing problems in specific complex diseases and health conditions like Alzheimer's disease, Parkinson's disease, and mental illness, we will focus on collecting fine-grained data, such as genotype-phenotype associations and brain region atrophy-phenotype associations and incorporating them to enrich our BKG, for the specific usage of these diseases.…”

Section: Focus On Specific Diseases On Health Conditionsmentioning

confidence: 99%

“…For example, COVID-KG [9] extracted COVID-19 specific information from biomedical literature and constructed a knowledge graph containing diseases, chemicals, and genes, along with their relationships. KGHC [10] is a knowledge graph focused on hepatocellular carcinoma. It extracted information from literature and contents on the internet, as well as structured triples from SemMedDB [11].…”

Section: Introductionmentioning

confidence: 99%

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Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH)

Hou

Guo

et al. 2021

Preprint

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The rapidly increasing biomedical knowledge, derived from biological experiments or gained from clinical practice, has become the important treasure in the biomedical research. The emerging knowledge graphs (KGs) provide an efficient and effective way to organize and retrieval the huge and increasing volume of biomedical knowledge. A biomedical KG (BKG) typically stores and represents knowledge by constructing a semantic network describing entities and the relationships between them. Previous efforts have been conducted to construct and curate BKGs by comprehensively integrating various biomedical data resources. Though the resulting BKGs have made a significant progress in this filed in advancing biological and medical research, there remain a big gap to a perfect one that is comprehensive and fine-grained enough. To this end, in the present study, we collected and integrated data from diverse well-curated biomedical knowledge bases and BKGs to curate a more comprehensive one, named the Cornell Biomedical Knowledge Hub (CBKH). To enhance the usage in accelerating biomedical research, we deployed CBKH using the famous graph database, Neo4j. This is a continuing effort and we are adding in more and more contents in CBKH to support the various complex needs in biomedical data analysis. Please contact us if you have better ideas and suggestions.

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Section: Focus On Specific Diseases On Health Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH)

Hou

Guo

et al. 2021

Preprint

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“…The sentiment classifier is unearthed by natural language processing and this technique mainly identifies on the n-gram. It is also a popular use of text representation for object categorization (Dou et al, 2018;Li et al, 2020). Some approaches used the graph to represent the word documents.…”

Section: Literaturementioning

confidence: 99%

“…The measurement of the graph consisted between two graph with label of liner Eq 2. It is used to calculate the maximum common sub graph withing maximum nodes like MSN function that provide the number of nodes that are contained in the maximum values (Li et al, 2020).…”

Section: Graph Similaritiesmentioning

confidence: 99%

Solicitation of Knowledge Graph Enhanced Neural Network Objects Detection by Sentiment Analysis

Conde¹

2021

ijmra

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In the machine learning technique, the knowledge graph is advancing swiftly; however, the basic models are not able to grasp all the affluence of the script that comes from the different personal web graphics, social media, ads, and diaries, etc., ignoring the semantic of the basic text identification. The knowledge graph provides a real way to extract structured knowledge from the texts and desire images of neural network, to expedite their semantics examination. In this study, we propose a new hybrid analytic approach for sentiment evaluation based on knowledge graphs, to identify the polarity of sentiment with positive and negative attitudes in short documents, particularly in 4 chirps. We used the tweets graphs, then the similarity of graph highlighted metrics and algorithm classification pertain sentimentality pre-dictions. This technique facilitates the explicability and clarifies the results in the knowledge graph. Also, we compare our differentiate the embeddings n-gram based on sentiment analysis and the result is indicated that our study can outperform classical n-gram models, with an F1-score of 89% and recall up to 90%.

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“…Li et al [ 12 ] proposed a novel fusion-embedding learning model, G2SKGE, which aims to learn the subgraph structure information of the entity in a knowledge graph. Li et al [ 13 ] proposed an approach to build a knowledge graph for hepatocellular carcinoma, and applied a biomedical information extraction system to filter and fuse the data.…”

Section: Introductionmentioning

confidence: 99%

Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma

Fang¹,

Lou²,

Hu³

et al. 2021

JMIR Med Inform

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Background Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma remain very difficult. Misdiagnosis and recurrence often occur, and experienced neurosurgeons are in serious shortage. A knowledge graph can help interns quickly understand the medical knowledge related to pituitary tumor. Objective The aim of this study was to develop a data fusion method suitable for medical data using data of pituitary adenomas integrated from different sources. The overall goal was to construct a knowledge graph for pituitary adenoma (KGPA) to be used for knowledge discovery. Methods A complete framework suitable for the construction of a medical knowledge graph was developed, which was used to build the KGPA. The schema of the KGPA was manually constructed. Information of pituitary adenoma was automatically extracted from Chinese electronic medical records (CEMRs) and medical websites through a conditional random field model and newly designed web wrappers. An entity fusion method is proposed based on the head-and-tail entity fusion model to fuse the data from heterogeneous sources. Results Data were extracted from 300 CEMRs of pituitary adenoma and 4 health portals. Entity fusion was carried out using the proposed data fusion model. The F1 scores of the head and tail entity fusions were 97.32% and 98.57%, respectively. Triples from the constructed KGPA were selected for evaluation, demonstrating 95.4% accuracy. Conclusions This paper introduces an approach to fuse triples extracted from heterogeneous data sources, which can be used to build a knowledge graph. The evaluation results showed that the data in the KGPA are of high quality. The constructed KGPA can help physicians in clinical practice.

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KGHC: a knowledge graph for hepatocellular carcinoma

Cited by 28 publications

References 34 publications

Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH)

Biomedical Discovery through the integrative Biomedical Knowledge Hub (iBKH)

Solicitation of Knowledge Graph Enhanced Neural Network Objects Detection by Sentiment Analysis

Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma

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