Structured Data Sources Research Articles

BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning.

Venous thromboembolism (VTE) is characterized by high morbidity, mortality, and complex treatment. A VTE knowledge graph (VTEKG) can effectively integrate VTE-related medical knowledge and offer an intuitive description and analysis of the relations between medical entities. However, current methods for constructing knowledge graphs typically suffer from error propagation and redundant information. In this study, we propose a deep learning-based joint extraction model, Biaffine Common-Sequence Self-Attention Linker (BCSLinker), for Chinese electronic medical records to address the issues mentioned above, which often occur when constructing a VTEKG. First, the Biaffine Common-Sequence Self-Attention (BCsSa) module is employed to create global matrices and extract entities and relations simultaneously, mitigating error propagation. Second, the multi-label cross-entropy loss is utilized to diminish the impact of redundant information and enhance information extraction. We used the electronic medical record data of VTE patients from a tertiary hospital, achieving an F1 score of 86.9% on BCSLinker. It outperforms the other joint entity and relation extraction models discussed in this study. In addition, we developed a question-answering system based on the VTEKG as a structured data source. This study has constructed a more accurate and comprehensive VTEKG that can provide reference for diagnosing, evaluating, and treating VTE as well as supporting patient self-care, which is of considerable clinical value.

Corporate Social Responsibility Practices of Global Automakers in Asia: Application of Carroll Model with Multi-Case Study Method

In spite of acknowledging the importance of sustainable and socially responsible practices; researchers have not been able to unanimously identify the priority areas of CSR for the automotive sector. This research was designed to understand the CSR activities of the global automakers in the Asian countries by applying the Carroll model wherein researchers wished to analyse the CSR programs of the global automakers, identify the priority CSR areas and assess the effect of CSR strategies on business performance of global automakers. Case study method was used in the existing study. This study utilised multiple case study research strategy whereby two automobile companies were studied in depth namely, Toyota and Volkswagen. The data for the study was secured from different- structured data sources like company reports, sustainability data records and media coverage. Data was analysed using content analysis. The study concluded that both companies are widely engaged in CSR initiatives that covered ethical, philanthropic, legal, and economic responsibilities. Toyota focused on their production process and policies that was employee centric, Volkswagen worked on industry modernization as well in their ethical practices. The philanthropic initiatives at both places worked towards education/training, employment generation, collaborations/partnerships, environmental and social welfare. Volkswagen also focused on sports and, art and cultural aspects in their humanitarian responsibilities. Both the peers were on the same page regarding economic practices where the cutting costs and improving efficiency was in focus. Both Toyota and Volkswagen gave more priority to the philanthropic and ethical aspect than the economic aspect. Both the companies experienced similar outcomes from these CSR initiatives like positive financial measures, quality metrics, brand identity improvement and differentiation, worker and consumer satisfaction, risk management and aversion, improved partnership opportunities.

Challenges, considerations, and approaches for developing a cost-effectiveness model for the adjuvant treatment of muscle-invasive urothelial carcinoma: with a spotlight on nivolumab versus placebo

Aims To present challenges and considerations for the development of a decision analytic model for evaluating the cost-effectiveness of adjuvant nivolumab compared with surveillance in patients with high-risk muscle-invasive urothelial carcinoma (MIUC) after radical resection. Methods and Results Alternative approaches related to both structural assumptions and data sources are presented to address challenges and data gaps, as well as discussion of strengths and limitations of each approach. Specifically, challenges and considerations related to the following are presented: (1) selection of a modeling approach (partitioned survival model or state transition model) given the available evidence, (2) choice of health state structure (three- or four-state) to model disease progression and subsequent therapy, (3) modeling of outcomes from subsequent therapy using tunnel states to account for time-dependent transition probabilities or absorbing health states with one-off costs and outcomes applied, and (4) methods for modeling health-state transitions in a setting where treatment has curative intent and available survival data are immature. Conclusions Multiple considerations must be taken into account when developing an economic model for new, emerging oncology treatments in early lines of therapy, all of which can affect the model’s overall ability to estimate (quality-adjusted) survival benefits over a lifetime horizon. This paper identifies a series of key structural and analytic considerations regarding modeling of nivolumab treatment in the adjuvant MIUC setting. Several alternative approaches with regard to structure and data have been included in a flexible cost-effectiveness model so the impact of the alternative approaches on model results can be explored. The impact of these alternative approaches on cost-effectiveness results and validation of outcomes predicted by the model will be presented in two companion articles. Our findings may also help inform the development of future models for other treatments and settings in early-stage cancer.

Pharmacovigilance in the digital era : A machine learning approach for early detection of adverse drug reactions

The revolutionary potential of machine learning (ML) in pharmacovigilance the early detection of adverse drug reactions (ADRs) in the digital era is examined in this study. It is difficult to promptly identify and address adverse drug reactions (ADRs) using traditional pharmacovigilance techniques. This study suggests an approach for combining structured and unstructured data sources for reliable ADR detection that makes use of machine learning. Many machine learning (ML) algorithms, including ensemble methods and neural networks, are used and contrasted with traditional techniques. Problems are tackled, such as ethical issues and the dependability of the data. Case studies present real-world implementations and shed light on how well ML models work. The study addresses the interpretability of these models, how to incorporate them into the current pharmacovigilance systems, and how data scientists and healthcare practitioners might work together. Upcoming developments and legal issues are highlighted in future directions. This study highlights how ML has the ability to completely transform pharmacovigilance by providing a proactive and more effective method of detecting ADRs and indicating a bright future for medication safety in the digital era.

Automatic semantic modeling of structured data sources with cross-modal retrieval

Analyzing and modeling the implicit semantic relationships in data sources is the key to achieving integration and sharing of heterogeneous data information. However, manual modeling of data semantics is a laborious and error-prone task that demands significant human effort and expertise. The paper proposes a novel explainable representation learning-based method that adopts an attention-based table-graph cross-modal retrieval model as a rating function during incremental search for automatic semantic modeling. Our supervised model utilizes the graph representation learning technique to extract latent semantics from data and aims to retrieve the most reliable semantic model for structured data sources. Experimental results demonstrate the effectiveness and robustness of our method.

Quality metrics at scale: Deriving time to first cancer treatment from electronic health records.

580 Background: Measurements of cancer care quality are critical for internal administration and participation in value-based care programs, towards the goal of improving patient outcomes. Data from electronic health records (EHRs) have the potential to allow for automatic computation of quality measures at a larger scale than manual chart review. This requires the creation of algorithms that process data and apply custom logic to define a clinically meaningful measurement. In this work, we describe the development and performance of an algorithm for deriving time from first primary oncologist visit to first cancer treatment using EHR data. Methods: This EHR-derived measure of time to first treatment (TTT) was implemented at an academic medical center for all patients who had a visit since 01/01/2011 and a cancer-related diagnosis code. We defined a patient’s primary oncologist as the first medical oncologist who they visited at least 5 times. These criteria were chosen to find the first provider at the site who the patient likely established care with and received treatment from. Then the patient’s TTT is computed as the number of days between their first visit date with this oncologist and the date of their first antineoplastic drug administration. Data on other treatment modalities, such as surgery and radiation therapy, were not available at the time of algorithm development. We allow for patients to receive their first treatment up to 60 days before their first visit to account for inpatient treatments before a primary outpatient oncologist is established. This algorithm’s performance was assessed through manual chart review among a sample of 38 patients. Results: Our algorithm correctly assigned 37 out of 38 patients (97.4%) to their primary oncologist, and computed the correct TTT for 28 out of 38 patients (73.4%). Incorrect TTT computations were mostly due to missingness of systemic therapy administrations in the structured EHR data; these were primarily for oral therapies. Conclusions: This work demonstrates the feasibility of measuring a patient’s TTT from structured EHR data alone. In particular, a patient’s initial visit with their primary oncologist can be derived with high accuracy. From this index date, the accuracy of the metric is dependent on the availability of structured treatment data. To further improve the EHR-derived metric, future work should incorporate other treatment modalities through structured data sources such as billing codes, and improve completeness of systemic therapy data in a scalable way, such as from natural language processing techniques.[Table: see text]

CrowdDA: Difficulty-aware crowdsourcing task optimization for cleaning web tables

Web tables are rich sources of structured data for collecting and analyzing data, but there exist various data quality problems such as missing or inconsistent values. Crowdsourcing provides a new solution that leverages the human cognitive ability to clean tables, but existing crowdsourcing-based solutions suffer from cleaning quality. For most crowd are not experts, the difficulty degree of cleaning tasks will seriously affect the cleaning result. To help people clean web tables effectively and efficiently, it is important to reduce the overall difficulty of tasks. In this paper, we introduce a difficulty-aware crowdsourcing task optimization system CrowdDA, which aims to recommend the best task execution order from easy to difficult for crowd and support various kinds of cleaning tasks for web tables. CrowdDA takes both latency and space constraints into account for task optimization and generates the task execution order that minimizes the overall difficulty of tasks under two constraints. Furthermore, CrowdDA adopts partition strategies for large tables to improve system efficiency, and introduces independent task sequence to tolerate crowd’s inconsistent answers for system robustness. The experiments based on the real-world datasets demonstrate the performance superiority of CrowdDA for improving the cleaning quality of web tables.

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.

CHV.br: Exploratory study for the development of a consumer health vocabulary (CHV) supported by a network model for Brazilian Portuguese language

Successful consumer health vocabulary (CHV) models have been engineered and updated by using automatic term extraction techniques from online content. However, the relationship between terms has yet to be mapped. This study aims to describe a CHV model for the Brazilian Portuguese language that is supported by a complex network. The method was split up into three distinct stages: (1) collect and automatically extract terms from structured data sources on the web, such as Unified Medical Language System (UMLS) vocabularies and DBpedia; (2) construct a complex network; and (3) select the terms supported by clustering techniques. A model called CHV.br was developed and supported by a complex network structure which makes connections between the controlled vocabulary and consumer vocabulary and maps semantic relationships as categories, synonyms and related terms. CHV.br contains 146,956 terms, of which 31,439 are UMLS preferred terms and 83,279 are synonyms. The CHV.br is available and powered by Simple Knowledge Organization System and Resource Description Framework standards. The method used in this study showed to be valid for the selection of the candidate terms by connecting the terms from different reliable resources, in addition to expanding the number of terms and their semantic relationships. The content and structure of CHV.br could play a vital role in enhancing the development of consumer-oriented health applications.

Автоматизация процесса подготовки производства земляных работ

Theoretical and materials for the creation of analog-discrete terrain models and their use in solving engineering problems are presented. With their help, it is possible to model areas of flooding, calculate the receipt of solar radiation, plan the construction of infrastructure facilities and other tasks. Various variants of the interpretation of the construction of analytical and discrete terrain models are considered. Block modules of algorithms for constructing, processing and visualizing analog terrain models are given. The samples of structured data sources for their creation are given. A relief modeling technique based on the use of logical-mathematical and matrix methods of big data processing is proposed.

ProVe: A pipeline for automated provenance verification of knowledge graphs against textual sources

Knowledge Graphs are repositories of information that gather data from a multitude of domains and sources in the form of semantic triples, serving as a source of structured data for various crucial applications in the modern web landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve as secondary sources of information and depend on well-documented and verifiable provenance to ensure their trustworthiness and usability. However, their ability to systematically assess and assure the quality of this provenance, most crucially whether it properly supports the graph’s information, relies mainly on manual processes that do not scale with size. ProVe aims at remedying this, consisting of a pipelined approach that automatically verifies whether a Knowledge Graph triple is supported by text extracted from its documented provenance. ProVe is intended to assist information curators and consists of four main steps involving rule-based methods and machine learning models: text extraction, triple verbalisation, sentence selection, and claim verification. ProVe is evaluated on a Wikidata dataset, achieving promising results overall and excellent performance on the binary classification task of detecting support from provenance, with 87.5 % accuracy and 82.9 % F1-macro on text-rich sources. The evaluation data and scripts used in this paper are available in GitHub and Figshare.

Epidemiological and clinicopathological features of patients (pts) with early- (EOPC) and late-onset pancreatic cancer (LOPC): A mono-institutional retrospective analysis.

10533 Background: PC is mostly diagnosed in the 7th decade of life. Recent evidence suggests a rising incidence of both EOPC and LOPC. However, there are few data about their specific characteristics. The aim of our study is to determine the incidence of EOPC and LOPC and their correlation with epidemiological and clinicopathological features. Methods: Real world data were extracted and analyzed by the Gemelli GENERATOR within the Gemelli Science and Technology Park (G-STeP). Study population was identified from health records matching main inclusion criteria: hospitalized pts with a diagnosis of PC (International Classification of Disease 9, ICD-9 codes captured from structured data source) or pts with at least one anatomic pathology report including PC evidence (selected using clinically validated text mining techniques from unstructured data source). Two subgroups were defined: EOPC ( < 50 years old) and LOPC ( > 75 years old) pts. Epidemiological and clinicopathological variables (gender; age, BMI, diabetes, history of pancreatitis, histology, primary tumor site, primary tumor surgery) were extracted using SAS (SAS(R) Institute suite for ETL). Statistical analyses were conducted using R (v4.2.1). Results: From January 2018 to December 2020, a total of 1606 PC pts treated at our Institution were included in the analysis. The median age was 69 years, 51% were male and the median BMI was 25. Regarding comorbidities, only 4% and 3% of pts had diabetes and history of pancreatitis, respectively. Adenocarcinoma (1120/1126; 99%) was the most common histotype and pancreatic head (576/901, 64%) was the most frequent primary tumor site. Two hundred and ninety-three pts (18%) underwent primary tumor surgery. Concerning the two subgroups, 92 (6%) pts had EOPC and 510 (32%) had LOPC. The median age was 43 years in EOPC group and 81 years in LOPC group. No difference between the two groups was found in gender (p 0.83), BMI (p 1) and tumor histology (p 1). Diabetes was present in 1% and 7% of EOPC and LOPC pts (p 0.07) and pancreatitis history in 3% and 2% (p 0.79), respectively. The primary tumor site was pancreatic head in 78% and 65% (p 0.06) of LOPC and EOPC, respectively. Regarding primary tumor surgery, a significant difference was observed between the two groups (27% in EOPC pts vs 11% LOPC pts; p < 0.001). Conclusions: In our study, we did not observe a different epidemiology or a distinctive clinicopathological profile between EOPC and LOPC pts. Despite the retrospective nature of our analysis and single-institution evaluation, our findings showed that EOPC pts more frequently underwent primary tumor surgery, probably related to better clinical conditions. Additional analyses on survival outcomes, molecular and genetic features may help to clarify differences between the two groups.

Italian real world artificial intelligence (AI) based analysis of early-onset colorectal cancer: The ERCOLE study.

e15539 Background: Early-onset colorectal cancer (EOCRC), defined as CRC diagnosed among individuals younger than 50 yrs, is becoming a public health issue, with rising incidence reported worldwide. In the US, EOCRC is the leading cause of cancer-related death among males aged 20-49 yrs and is second among female aged 40-49 yrs. Italy lacks a national cancer registry and available data regard mostly northern regions. Fondazione Policlinico Gemelli (FPG) Hospital is a national referral center for CRC (especially for middle and southern regions), ranking first for surgical procedures. Therefore, mining incidence rate at FPG Hospital contributes to provide a full picture of EOCRC incidence trend in Italy. Methods: The objective of this study was to assess the trends of EOCRC incidence at FPG along 15 yrs. Real world data collected from everyday clinical practice in the FPG Hospital Data Warehouse were extracted within the Gemelli GENERATOR framework. Study population was identified from records of pts from 2005 to 2019 matching two main inclusion criteria: pts hospitalized with a diagnosis of CRC (ICD-9 codes at discharge included in 153.* and 154. *, captured form structured data source); or pts with at least one pathology report of CRC (selected using clinically-validated text mining techniques from unstructured data source). Variables of interest, including demographics and surgical procedures, were extracted using SAS (SAS(R) Institute suite for ETL). Study population was stratified according to age (≤50yrs; > 50yrs), gender and tumor site. Primary endpoint was the annual percent change (APC). APC was estimated using the joint regression method with a maximum of 2 joint points allowed. Statistical analyses were conducted using R (v4.2.1). Results: 18606 consecutive single pts were included, of those 10.9% were EOCRC. From 2005 to 2019 a statistically significant APC increase for EOCRC was observed overall (9.2, CI 6.5-11.9, p < .001) and regardless of gender (male: 8.0, CI 4.7-11.3, p < .001; female: 10.1, CI 7.5-12.8, p < .001). Splitting the timeframe, a major trend was observed between 2010 and 2013 (APC: 30.5, 34.4 and 27.5, respectively overall, in male and in female pts). Primary tumor location was available for 10690 (of those 9.3% were EOCRC). Analyzing primary tumor location, a trend toward increased and decreased APC for EOCRC was observed for R-sided (3.6, p .105) and L-sided (-2.0, p .312) tumors, respectively, while a statistically significant increase was observed for rectum (4.5, p .02), specifically between 2012 and 2019 (13.1, p .02). Conclusions: This AI-based real world analysis confirms a rising incidence for EOCRC in Italy. Data has solid external consistency. Further analyses on familial history, life styles and MMR status are planned. The appropriate threshold age to start the screening needs to be addressed.

A practical approach to constructing a knowledge graph for soil ecological research

With the rapid accumulation of biodiversity data, data integration has emerged as a hot topic in soil ecology. Data integration has indeed the potential to advance our knowledge of global patterns in soil biodiversity by facilitating large-scale meta-analytical studies of soil ecosystems. However, ecologists are still poorly equipped when it comes to integrating disparate datasets. In recent years, knowledge graphs have emerged as a powerful tool for integrating large amounts of distributed heterogeneous data while making these data more easily interpretable by humans and computers. This paper presents a practical approach to constructing a biodiversity knowledge graph from heterogeneous and distributed (semi-)structured data sources. To illustrate our approach, we integrate several datasets on the trophic ecology of soil organisms into a trophic knowledge graph and show how both explicit and implicit information can be retrieved from the graph to support multi-trophic studies.

Applying the LOT Methodology to a Public Bus Transport Ontology aligned with Transmodel: Challenges and Results

We present an ontology that describes the domain of Public Transport by bus, which is common in cities around the world. This ontology is aligned to Transmodel, a reference model which is available as a UML specification and which was developed to foster interoperability of data about transport systems across Europe. The alignment with this non-ontological resource required the adaptation of the Linked Open Terms (LOT) methodology, which has been used by our team as the methodological framework for the development of many ontologies used for the publication of open city data. The ontology is structured into three main modules: (1) agencies, operators and the lines that they manage, (2) lines, routes, stops and journey patterns, and (3) planned vehicle journeys with their timetables and service calendars. Besides reusing Transmodel concepts, the ontology also reuses common ontology design patterns from GeoSPARQL and the SOSA ontology. As part of the LOT data-driven validation stage, RDF data has been generated taking as input the GTFS feeds (General Transit Feed Specification) provided by the Madrid public bus transport provider (EMT). Mapping rules from structured data sources to RDF were developed using the RDF Mapping Language (RML) to generate RDF data, and queries corresponding to competency questions were tested.

Omics data integration facilitates target selection for new antiparasitic drugs against TriTryp infections.

Introduction: Trypanosoma cruzi, Trypanosoma brucei, and Leishmania spp., commonly referred to as TriTryps, are a group of protozoan parasites that cause important human diseases affecting millions of people belonging to the most vulnerable populations worldwide. Current treatments have limited efficiencies and can cause serious side effects, so there is an urgent need to develop new control strategies. Presently, the identification and prioritization of appropriate targets can be aided by integrative genomic and computational approaches. Methods: In this work, we conducted a genome-wide multidimensional data integration strategy to prioritize drug targets. We included genomic, transcriptomic, metabolic, and protein structural data sources, to delineate candidate proteins with relevant features for target selection in drug development. Results and Discussion: Our final ranked list includes proteins shared by TriTryps and covers a range of biological functions including essential proteins for parasite survival or growth, oxidative stress-related enzymes, virulence factors, and proteins that are exclusive to these parasites. Our strategy found previously described candidates, which validates our approach as well as new proteins that can be attractive targets to consider during the initial steps of drug discovery.

Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5min. We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.

Drug abuse is a serious problem in the United States, with over 90,000 drug overdose deaths nationally in 2020. A key step in combating drug abuse is detecting, monitoring, and characterizing its trends over time and location, also known as pharmacovigilance. While federal reporting systems accomplish this to a degree, they often have high latency and incomplete coverage. Social-media-based pharmacovigilance has zero latency, is easily accessible and unfiltered, and benefits from drug users being willing to share their experiences online pseudo-anonymously. However, unlike highly structured official data sources, social media text is rife with misspellings and slang, making automated analysis difficult. Generative Pretrained Transformer 3 (GPT-3) is a large autoregressive language model specialized for few-shot learning that was trained on text from the entire internet. We demonstrate that GPT-3 can be used to generate slang and common misspellings of terms for drugs of abuse. We repeatedly queried GPT-3 for synonyms of drugs of abuse and filtered the generated terms using automated Google searches and cross-references to known drug names. When generated terms for alprazolam were manually labeled, we found that our method produced 269 synonyms for alprazolam, 221 of which were new discoveries not included in an existing drug lexicon for social media. We repeated this process for 98 drugs of abuse, of which 22 are widely-discussed drugs of abuse, building a lexicon of colloquial drug synonyms that can be used for pharmacovigilance on social media.

Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists.

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform ( https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock's ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

Implementation of a framework for graph-based keyword search over relational data

The challenge of easily interconnecting and exploiting the increasing amount of data from structured data sources is still a primary concern for researchers from the industry and the academy. Keyword search over structured data systems (KSS) has attracted much interest as it provides a simple interface to query structured data. At the best of our knowledge, KSS evolved neither into a standard model nor into a commercial product. The implementation of every new system published so far was isolated from the previous systems, even when it advanced the state of the art for a single aspect. Also, the source code of these systems is not shared and the experimental results are not easily replicable. The present study aims at filling this gap, by the design and the shared implementation of a unified framework for graph-based KSS.