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graph4Med: a web application and a graph database for visualizing and analyzing medical databases

BMC BIOINFORMATICS 2022年第1期23卷 1-22页

作者： Schafer, Jero Tang, Ming Luu, Danny Bergmann, Anke Katharina Wiese, Lena Goethe Univ Frankfurt Inst Comp Sci Frankfurt Germany Hannover Med Sch Dept Human Genet Hannover Germany Leibniz Univ Hannover L3S Res Ctr Hannover Germany Fraunhofer ITEM Bioinformat Grp Hannover Germany

BackgroundMedical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated;hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit. DescriptionWe present graph4Med, a web application that relies on a graph database obtained by transforming a relational database. graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients' health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients' cohort analysis. This way our tool (1) quickly displays the overview of patients' cohort information such as distributions of gender, age, mutations (fusions), diagnosis;(2) provides mutation (fusion) based similarity search and display in a maneuverable graph;(3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients. ConclusionWe demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization

关键词： graph database Medical database Data exploration Visualization Web application

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Cover-up: a probabilistic privacy-preserving graph database model

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第11期14卷 15003-15010页

作者： Stokes, Klara Natl Univ Ireland Maynooth Maynooth Kildare Ireland

A new graph database model is introduced that allows for an efficient and straightforward privacy-preserving mechanism. A probabilistic graph database model is also proposed, perhaps less suitable for lossless storage, but adapted for the use of statistical analysis that preserves the privacy of the individuals behind the data. Parallels are drawn to concepts in combinatorics such as clique complexes and incidence geometries.

关键词： Privacy graph database Clique complex Incidence geometry

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Unified platform for storing, retrieving, and analysing biomechanical applications data using graph database

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JOURNAL OF BIG DATA 2023年第1期10卷 1-18页

作者： Hribernik, Matevz Tomazic, Saso Umek, Anton Kos, Anton Univ Ljubljana Fac Elect Engn Trzaska Cesta 25 Ljubljana 1000 Slovenia

Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence

关键词： Biomechanical application Sensor systems Cloud platform Big data Data modelling graph database Application programming interface Sport Rehabilitation Connected devices

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A spatially-pruned vertex expansion operator in the Neo4j graph database system

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GEOINFORMATICA 2019年第3期23卷 397-423页

作者： Sun, Yuhan Sarwat, Mohamed Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA Arizona State Univ Comp Sci Tempe AZ USA Arizona State Univ Data Syst Data Sys Lab Tempe AZ USA

graphs are widely used to model data in many application domains. Thanks to the wide spread use of GPS-enabled devices, many applications assign spatial attributes to graph vertexes (e.g., geographic knowledge bases, geo-tagged social media). graph database systems such as Neo4j and Titan are commonly used to manage and query graph data. Even though an off-the-shelf graph database system allows users to define spatial location semantics on vertexes and edges, existing graph query processors are not natively optimized for spatial predicates. The paper proposes GEOEXPAND-a query operator that adds spatial data awareness to a graph database management system. GEOEXPAND allows efficient execution of graph queries that involve spatial predicates. The proposed operator makes use of spatial bitmap entries stored as properties in the graph. GEOEXPAND leverages such bitmap entries to possibly terminate the graph traversal process early and hence reduces the query latency. Since the spatial bitmap entries are represented using a light-weight data structure, they do not add a lot of storage or maintenance overhead. That makes GEOEXPAND a practical solution. Experiments based on implementation inside the core kernel of Neo4j prove that the GEOEXPAND operator exhibits up to two orders of magnitude better query response time than the classic Expand operator used in Neo4j.

关键词： Spatial data graph database Spatial index

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Subgraph Matching with Set Similarity in a Large graph database

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2015年第9期27卷 2507-2521页

作者： Hong, Liang Zou, Lei Lian, Xiang Yu, Philip S. Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China Univ Texas Pan Amer Dept Comp Sci Edinburg TX 78539 USA Univ Illinois Dept Comp Sci Chicago IL 60607 USA Tsinghua Univ Inst Data Sci Beijing 100084 Peoples R China

In real-world graphs such as social networks, Semantic Web and biological networks, each vertex usually contains rich information, which can be modeled by a set of tokens or elements. In this paper, we study a subgraph matching with set similarity (SMS2) query over a large graph database, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic) weighted set similarity. To efficiently process the SMS2 query, this paper designs a novel lattice-based index for data graph, and lightweight signatures for both query vertices and data vertices. Based on the index and signatures, we propose an efficient two-phase pruning strategy including set similarity pruning and structure-based pruning, which exploits the unique features of both (dynamic) weighted set similarity and graph topology. We also propose an efficient dominating-set-based subgraph matching algorithm guided by a dominating set selection algorithm to achieve better query performance. Extensive experiments on both real and synthetic datasets demonstrate that our method outperforms state-of-the-art methods by an order of magnitude.

关键词： Subgraph matching set similarity graph database index

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The graph database Jack of All Trades or Just Not SQL?

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IT PROFESSIONAL 2017年第6期19卷 21-25页

作者： Hurlburt, George F. Thiruvathukal, George K. Lee, Maria R. STEMCorp Sao Paulo Brazil Loyola Univ Chicago Comp Sci Chicago IL USA Shih Chien Univ Informat Technol & Management Taipei Taiwan

This special issue of IT Professional focuses on the graph database. The graph database, a relatively new phenomenon, is well suited to the burgeoning information era in which we are increasingly becoming immersed. Here, the guest editors briefly explain how a graph database works, its relation to the relational database management system (RDBMS), and its quantitative and qualitative pros and cons, including how graph databases can be harnessed in a hybrid environment. They also survey the excellent articles submitted for this special issue.

关键词： Special Issues And Sections graphical Models databases graph database Big Data No SQL Relational database

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Diverse Mentoring Connections Across Institutional Boundaries in the Biomedical Sciences: Innovative graph database Analysis

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JOURNAL OF MEDICAL INTERNET RESEARCH 2024年第1期26卷 e47560页

作者： Syed, Toufeeq Ahmed Thompson, Erika L. Latif, Zainab Johnson, Jay Javier, Damaris Stinson, Katie Saleh, Gabrielle Vishwanatha, Jamboor K. Univ Texas Hlth Sci Ctr Houston 7000 Fannin StSuite 600 Houston TX 77030 USA Univ Texas Hlth & Sci Ctr San Antonio San Antonio TX USA Northwestern Univ Evanston IL USA Univ North Texas Hlth Sci Ctr Ft Worth Ft Worth TX USA

Background: With an overarching goal of increasing diversity and inclusion in biomedical sciences, the National Research Mentoring Network (NRMN) developed a web -based national mentoring platform (MyNRMN) that seeks to connect mentors and mentees to support the persistence of underrepresented minorities in the biomedical sciences. As of May 15, 2024, the MyNRMN platform, which provides mentoring, networking, and professional development tools, has facilitated more than 12,100 unique mentoring connections between faculty, students, and researchers in the biomedical domain. Objective: This study aimed to examine the large-scale mentoring connections facilitated by our web -based platform between students (mentees) and faculty (mentors) across institutional and geographic boundaries. Using an innovative graph database, we analyzed diverse mentoring connections between mentors and mentees across demographic characteristics in the biomedical sciences. Methods: Through the MyNRMN platform, we observed profile data and analyzed mentoring connections made between students and faculty across institutional boundaries by race, ethnicity, gender, institution type, and educational attainment between July 1, 2016, and May 31, 2021. Results: In total, there were 15,024 connections with 2222 mentees and 1652 mentors across 1625 institutions contributing data. Female mentees participated in the highest number of connections (3996/6108, 65%), whereas female mentors participated in 58% (5206/8916) of the connections. Black mentees made up 38% (2297/6108) of the connections, whereas White mentors participated in 56% (5036/8916) of the connections. Mentees were predominately from institutions classified as Research 1 (R1;doctoral universities-very high research activity) and historically Black colleges and universities (556/2222, 25% and 307/2222, 14%, respectively), whereas 31% (504/1652) of mentors were from R1 institutions. Conclusions: To date, the utility of mentoring connections

关键词： online platform mentorship diversity network analysis graph database online communities

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Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

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CHEMICAL BIOLOGY & DRUG DESIGN 2022年第2期100卷 169-184页

作者： Murali, Vidhya Muralidhar, Y. Pradyumna Koenigs, Cassandra Nair, Meera Madhu, Sethulekshmi Nedungadi, Prema Srinivasa, Gowri Athri, Prashanth Amrita Sch Engn Dept Comp Sci & Engn Bengaluru India PES Univ PES Ctr Pattern Recognit Dept Comp Sci & Engn Bengaluru India Bielefeld Univ Bioinformat & Med Informat Northrhine Westphalia Germany Amrita Vishwa Vidyapeetham Amrita Sch Biotechnol Amritapuri Kerala India Amrita Sch Engn Dept Comp Sci & Engn Vallikavu Kerala India

The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. In the above list, biological activities have never been used as an independent variable towards the prediction of clinical trial outcomes. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the "Pass" class. "Pass" refers to one of the two classes (Pass/Fail) of all clinical trials, and the model performed well in predicting the "Pass" category. Through the analysis of feature contributions to predictive capability, we have demonstrated that bioactivity plays a statistically significant role in predicting clinical trial outcome. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. Cleaned, organized, integrated data and code to map these entities, created as a part of this work, are available open-source. This reproducibility and the freely available code ensure that researchers with access to deep curated and proprietary clinical trial databases (we only use open-source data in this study) can further expand the scope of the results.

关键词： bioactivity clinical trial data integration ensemble algorithms graph database machine learning

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Social data provenance framework based on zero-information loss graph database

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SOCIAL NETWORK ANALYSIS AND MINING 2022年第1期12卷 1-25页

作者： Rani, Asma Goyal, Navneet Gadia, Shashi K. DBRAIT Dept CSE Port Blair A&n Islands India BITS Pilani CSIS Dept Pilani Rajasthan India Iowa State Univ Dept CS Ames IA USA

Social media has become a common platform for global communication across the world due to its rapid dissemination of information among a large audience. Its popularity has raised a crucial challenge to capture the social data provenance of a piece of information published on social media. Social data provenance describes the source and deriving process of a digital content, and when it is updated since its existence? It aids in determining reliability, authenticity, and trustworthiness of a piece of information and explaining how, when, and by whom this information is published. In this paper, we propose a social data provenance (SDP) framework based on zero-information loss graph database (ZILGDB). The proposed framework supports historical data queries, and querying through time using updates management in ZILGDB. It has the capability to capture provenance for a query set including select, aggregate, and data update queries with insert, delete, and update operations. It also provides a detailed provenance analysis through visualization and with efficient multi-depth provenance querying support, to determine both direct and indirect sources of a digital content. We conduct a real-life use case study to evaluate the usefulness of proposed framework in terrorist attack investigation. We evaluate the performance of proposed framework in terms of average execution time for various provenance queries, and provenance capturing overhead for a query set.

关键词： Social media Social data provenance graph database ZILGDB Provenance querying

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Usage of a graph database for the selection of sterile items in the OR

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2023年第5期18卷 871-875页

作者： Mueller, C. Bernhard, L. Wilhelm, D. Tech Univ Munich Klinikum Rechts Isar Res Grp MITI Munich Germany Tech Univ Munich Dept Surg Klinikum Rechts Isar Munich Germany

Purpose In this work, we present a subsystem of a robotic circulating nurse, that produces recommendations for the next supplied sterile item based on incomplete requests from the sterile OR staff, the current situation, predefined knowledge and experience from previous surgeries. We describe a structure to store and query the underlying information in terms of entities and their relationships of varying strength. Methods For the implementation, the graph database Neo4j is used as a core component together with its querying language Cypher. We outline a specific structure of nodes and relationships, i.e., a graph. Primarily, it allows to represent entities like surgeons, surgery types and items, as well as their complex interconnectivity. In addition, it enables to match given situations and partial requests in the OR with corresponding subgraphs. The subgraphs provide suitable sterile items and allow to prioritize them according to their utilization frequency. Results The graph database was populated with existing data from 854 surgeries describing the intraoperative use of sterile items. A test scenario is evaluated in which a request for "Prolene " is made during a cholecystectomy. The software identifies a specific "Prolene " suture material as the most probable requested sterile item, because of its utilization frequency from over 95%. Other "Prolene " suture materials were used in less than 15% of the cholecystectomies. Conclusion We have proposed a graph database for the selection of sterile items in the operating room. The example shows how the partial information from different sources can be easily integrated in a query, leading to an unique result. Eventually, we propose possible enhancements to further improve the quality of the recommendations. In the next step, the recommendations of the software will be evaluated in real time during surgeries.

关键词： graph database User interface Sterile items Surgical workflow Digital operating room

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