Jointly Integrating VCF-Based Variants and OWL-Based Biomedical Ontologies in MongoDB

Liu, Jian; Qu, Zhi Yong; Yang, Ming; Sun, Jialiang; Su, Shuhui; Zhang, Lei

doi:10.1109/tcbb.2019.2951137

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…Attribute information matching means fusion matching of attribute fields of vector data, and this method can remove duplicate fields and fuse multiple types of fields into the same element to achieve efficient utilization of vector data and storage space. [15] By using semantic matching to match the attribute information, the matching process is shown in Figure 2. For the fields to be matched, firstly, by selecting specific fields for semantic matching, in order to prevent mis-matching in the matching process, multiple matching fields are selected.…”

Section: Property Information Matchingmentioning

confidence: 99%

Modeling technology for fusion of massive strip 3D terrain and feature models

Li,

Feng,

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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In order to solve the problem that traditional GIS methods are difficult to handle massive data, complex spatial relationships and accurate geographic phenomenon modeling. By fusing terrain and feature models, geographic phenomena can be efficiently and accurately represented and analyzed to provide a reliable basis for decision making.This paper proposes a fusion modeling technique for massive strip 3D terrain and feature models, aiming to solve the problem of effective fusion and modeling of 3D terrain and feature models for strip data in large-scale spatial data processing. Based on open-source geographic information technology, the traditional method is designed to unify and model 2D vector data, raster data, BIM data and 3D data from different sources, formats and scales to form a standardized spatial data set, in response to the problems of high computational complexity, large storage requirements and data redundancy in processing massive data. For the characteristics of strip data and 3D terrain and feature models, geometric matching and semantic matching methods are used to fuse and process vector data representing the same entity with different attribute information and spatial location information to solve the problem of data redundancy and duplication; it is used to describe the different levels and precision of spatial data, and the appropriate data precision can be flexibly selected in the fusion modeling process, so as to achieve a balance between data quality and calculation The purpose of balancing data quality and computational efficiency. Based on LOD hierarchical detail technology fusion processing of terrain-independent BIM models, terrain-dependent fusion of linear 3D feature models, to achieve the surface fitting of multi-source 3D model data in a specific scene, to improve the 3D scene display effect.In terms of data processing efficiency: traditional GIS methods may face bottlenecks in computation and storage when dealing with massive data. The efficiency of data processing can be improved by adopting fusion modeling technology for massive data. For spatial relationship accuracy: fusion modeling technology can capture the complex spatial relationships between terrain and features more accurately, providing more accurate simulation and analysis results.

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Section: Property Information Matchingmentioning

confidence: 99%

Modeling technology for fusion of massive strip 3D terrain and feature models

Li,

Feng,

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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“…A wide variety of algorithmic models are available for ontology generation, and each of these models are applied to different elds for application-speci c deployments. For instance, the work in [3,4] proposes industrial ontology generation with domain identi cation support, and biomedical ontology generation (BOG) for Variant Call Formats (VCFs). These models have high e ciency of data representation and require low delay due to their application-speci c deployment behaviour.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Purbey

Khandelwal

Choudhary³

2023

SIViP

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Ontology generation is a process of relationship analysis, and representation for multiple data categories using automatic or semi-automatic approaches. This process requires a domain knowledgebase that describes given input data using entity-to-entity relations. A wide variety of approaches are proposed for this purpose, and each of them processes & converts input data using multiple relationship evaluation stages. These stages include data-preprocessing, correlation analysis, entity mapping, and ontology generation. A very few of these approaches are dataset independent, and most of them do not implement security measures during ontology generation, which limits their security, scalability & deployment capabilities during real-time implementation. Thus, in this text a blockchain based secure & e cient ontology generation model for multiple data genres using augmented strati cation (BOGMAS) is described. The BOGMAS model uses a semi-supervised approach for ontology generation from almost any structured or unstructured dataset. It uses a variance-based method (VBM) for reduction of redundant numerical features from the dataset, while textual features are converted to numerical values via standard word2vec model, and then processed using VBM. This model uses a combination of linear support vector machine (LSVM), and extra trees (ET) strati ers for variance estimation, which makes the model highly e cient, and reduces redundant features from the output ontology. These feature sets & their variances are given to a correlation engine for relationship estimation, and ontology generation.Each ontology record is secured using a mutable proof-of-work (PoW) based blockchain model, which assists in imbibing transparency, traceability, and distributed peer-to-peer processing capabilities. The generated ontology is represented using an incremental OWL (W3C Web Ontology Language) format, which assists in dynamically sizing the ontology depending upon incoming data. Performance of the proposed BOGMAS model is evaluated in terms of precision & recall of representation, memory usage, computational complexity, and accuracy of attack detection. It is observed that the proposed model is highly e cient in terms of precision, recall & accuracy performance, but has incrementally higher computational complexity & delay of ontology formation when compared with existing approaches. Due to this incremental increase in delay, the proposed model is observed to be applicable for a wide variety of real-time scenarios, which include but are not limited to, medical ontology generation, sports ontology generation, and internet of things (IoT) ontology generation with high security levels.

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“…All database files were collected, processed and stored as JavaScript Object Notation (JSON) files in MongoDB database [41]. MongoDB is an open-source document-oriented (NoSQL) database [41,42]. The variables and query functions of the application program interface (API) in the Human.miRFFL.DB web application are defined in the python language.…”

Section: Database Developmentmentioning

confidence: 99%

Human.miRFFL.DB-A curated resource for human miRNA coregulatory networks and associated regulatory-circuits

Khurana

Varshney

Sugadev

et al. 2020

Preprint

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AbstractmiRNA and Transcrition Factors (TFs) are fundamental transcriptional and post-transcriptional regulators of the gene expression respectively, controlling important biological processes. Gene regulation is viewed as a complex process where regulatory elements and their targets form highly complex network interactions. In recent years, many high throughput studies revealed that these complex regulatory interactions are mediated by the complex interplay between miRNA and TF regulating a common Target Gene (TG) in conjunction. Moreover, miRNAs and TFs also known to regulate each other. This complex coregulatory mechanism may be represented in the form of miRNA:TF:TG coregulatory network, which can be used to identify several small recurring subgraphs known as motifs. One of these motifs, called the Feed-Forward Loops (FFLs) is a three-node pattern which is composed of a miRNA and a TF, one of which regulates the other and both jointly regulating a TG. FFLs have been proven useful in elucidating the complex interplay of gene regulation during many physiological and pathological conditions. Hence we have developed miRFFLDB, a comprehensive integrated resource for identification and visualization of human miRNA:TF:TG coregulatory directed networks and their associated FFLs. In house scripts based on the graph theory principles have been used to identify FFL motifs i.e. miRNA-FFL and TF-FFL. miRFFLDB also provides interactive visualization of these coregulatory networks and their associated FFLs. miRFFLDB can be used as a comprehensive ready-reference for human miRNA:TF:TG coregulatory networks and associated FFLs for decrypting complex cellular interactions of these regulatory biomolecules at the molecular level. miRFFLDB is available online at http://www.mirffldb.in.HighlightsmicroRNA (miRNA) and Transcriptional Factors (TFs) are two important regulatory biomolecules and a complex interplay between the two are known to control a Target Gene (TG).The tripartite motif of TF, miRNA regulating a common TG is known as Feed Forward Loop (FFL) or regulons.Identification of FFL motifs help to decode the interplay between miRNAs, TFs and TGs and get new mechanistic insights into complex molecular and cellular regulation processes in various multifactorial disorders.miRFFLDB is a comprehensive repository of miRNA:TF:TG co-regulatory network and associated FFLs of 2,596 human miRNAs.The server characterizes the miRNA–Target Interactions (MTIs) as miRNA:TG and miRNA:TF interactions. In addition, TF:TG and TF:miRNA interactions are added, which helps in exemplifying the network as miRNA:TF:TG co-regulatory directed network.The server further identifies miRNA-FFLs and TF-FFLs tripartite motifs from the co-regulatory networks using in-house scripts based on graph theory principles. The server also provides an interactive visualization of coregulatory networks and their associated FFLs.FFL motif identification is a powerful metanalysis tool for predicting novel miRNA, TF, genes candidates for prognostic, therapeutic, diagnostic purposes.

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Jointly Integrating VCF-Based Variants and OWL-Based Biomedical Ontologies in MongoDB

Cited by 8 publications

References 42 publications

Modeling technology for fusion of massive strip 3D terrain and feature models

Modeling technology for fusion of massive strip 3D terrain and feature models

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

Human.miRFFL.DB-A curated resource for human miRNA coregulatory networks and associated regulatory-circuits

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