A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets

Abstract

Background Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue. Methods First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets. Results and conclusions We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.

Machine and cognitive intelligence for human health: systematic review

“…For example, to minimize the time and effort required for technical (e.g., image annotation) and legal tasks (e.g., de-identification), Trägårdh et al [ 81 ] used CNNs to segment organs in computed tomography to “extract standardized uptake values from the corresponding positron emission tomography images (p. 1)”. In relationship mining, Zhang et al [ 82 ] mined putative disorder–gene–drug relations concerning Parkinson's diseases using a gene–disorder–drug semantic relationship mining algorithm that queried the relations among a variety of entities from varied data sources. With the use of ontologies and semantic Web, Traverso et al [ 14 ] developed prediction algorithms for personalized therapy by proposing a scalable big data architecture “based on data standardization to transform clinical data into findable, accessible, interoperable and reusable data (p. 854)”.…”

“…Second, there are scholars focusing on correlation analysis, including: (1) conducting correlation analysis for the individual facial action units to understand the decoupling of these individual features [ 47 ], and (2) demonstrating potential correlations between “a person’s descriptions about wartime experiences in their blogs with the ensuing symptoms or disorders via Focus groups and medical records analysis (p. 6) [ 13 ]”. Third, scholars are also encouraged to: (1) add functionality for real-time image annotation during meetings and make the transition to Internet-based telephone services [ 34 ], (2) adopt a health-related misinformation detection framework to English health misinformation detection [ 86 ], (3) explore repositioning drugs according to semantic relations for varied syndromes, for example, Parkinson’s diseases, Alzheimer’s diseases, and cancers [ 82 ], (4) facilitate the extraction of date and confirmed-case counts [ 77 ], (5) propose approaches for predicting user dropout rate to provide timely interventions accordingly [ 64 ], (6) extend system functionality by providing automatic graph-based summarization of input texts [ 56 ], (7) investigate “clustering solutions with a larger number of clusters or implementing additional features in the cluster analysis to represent other dimensions of participant experience (p. 14)” for richer characterization of participant experiences for personalization [ 61 ], (8) perform more intensive label harmonization using common data model ontologies [ 54 ], and (9) conduct syntactic analysis of natural language questions and test syntactic dependencies’ contribution on confirming previously extracted semantic relationships and detecting unfamiliar relationships [ 30 ]. Other directions include: (1) promoting “comprehensive care by establishing additional applications for home follow-ups and working with the children with the rare inherited disorders and their families (p. 11) [ 95 ]”, (2) developing neural-driven security solutions for multimedia data like color medical images, audios, and videos to be stored in the cloud [ 39 ], (3) updating parameters dynamically [ 96 ], and (4) improving predictors to reduce prediction bias to discover physiological mechanisms of ion channel-targeted conotoxins [ 41 ].…”

Informatics and machine learning methods for health applications

“…In five of these articles [ 1 , 4 , 7 , 9 , 10 ], electronic health record (EHR) data were analyzed for various purposes, including (1) predicting outcomes such as mortality [ 1 , 7 , 10 ], sepsis [ 7 ], and preterm birth [ 10 ], (2) annotation and extraction of age and temporally-related events [ 4 ], and (3) sepsis phenotyping [ 9 ]. In the remaining five articles [ 2 , 3 , 5 , 6 , 8 ], the authors analyzed (1) publication data for extracting biomedical concepts [ 2 ], (2) wearable sensor data for stress detection [ 3 ], (3) location data for resource allocation for cardiac emergency [ 5 ], (4) social media data for drug use detection [ 6 ], and (5) ECG data for biomedical signal denoising [ 8 ].…”

“…In these studies, the authors employed informatics and machine learning methods to address various health topics, including diabetes [ 1 ], autism spectrum disorder [ 2 ], stress [ 3 ], health research in general [ 4 ], cardiac arrest [ 5 ], drug use [ 6 ], sepsis [ 7 , 9 ], heart disorders [ 8 ], and preterm birth and perinatal mortality [ 10 ]. To address the biomedical problems in the above health applications, these studies employed a wide range of informatics and machine learning methods, including deep learning [ 1 , 3 , 6 , 7 ], NLP [ 1 , 2 , 4 ], matching algorithms [ 5 ], association mining [ 6 ], wavelet analysis [ 8 ], factor analysis [ 9 ], frequent graph mining [ 9 ], and traditional statistical machine learning [ 10 ].…”

“…In “ SURF: identifying and allocating resources during out-of-hospital cardiac arrest ”, Rao et al [ 5 ] studied a significant clinical problem, which is to optimize the resource allocation in the public setting for treating patients with sudden cardiac arrest. They presented a conceptual framework to analyze the needs for rapid response and the involved users as well as their workflow.…”