Aggregate Data Research Articles

Data resource profile: Exploring freely accessible data describing wider determinants of health in England

IntroductionIn England, life expectancy has stalled and significant decreases observed in certain geographical areas and populations. The cause of this involves complex dynamics between an individual's health, characteristics, lifestyle, and their wider environment known as the wider determinants of health which are key to good life expectancy, healthy life expectancy, and prevention of long-term medical conditions. Knowing the availability, breadth, features, and linkage potential of datasets relevant to wider determinants of health is important for exploring trends and associations for policy and public health planning. MethodsA systematic mapping of internet content identified accessible datasets relevant to wider determinants of health in England with town level geographical granularity or lower. Search terms were used in search engines and chatbots to identify weblinks subsequently examined for eligible datasets. Results105 potential weblinks to datasets were identified. Of these, twenty-one weblinks were explored further after exclusion of those: not accessible or currently live (n = 13); duplicated across search engines (n = 17); providing information only (i.e. no raw data, n = 14); did not provide freely accessible data (n 3); were not relevant to wider determinants of health (n = 17); lacked geographical granularity (n = 26). Eighty-nine datasets of interest were compiled with sub-town level data aggregation. Approximately half (n = 47, 52%) were from the England and Wales census 2021, with the remaining sources including government bodies, public services, and research datasets. Datasets covered many valuable categories of wider determinants of health. Key data gaps included food consumption, social care data and community/voluntary services. ConclusionIn England, access to data relevant to wider determinants of health is good and available at relatively small geographical resolution. Accessible datasets were identified and compiled within multiple categories of wider determinants of health as a useful data resource to explore wider determinants of health at place if linked to relevant health data or population studies. Key Features This data resource profile describes a systematic mapping of freely accessible population data on wider determinants of health in England. To the authors knowledge this is the first comprehensive compilation of freely accessible data resources of this kind. This data resource profile was created to support research into the mechanisms and impact of wider determinants on the health of populations in England but is applicable to research and populations studies wider than this. Eighty-nine datasets were identified that may be of use to researchers in health and other population data fields. Datasets are held separately but many have the potential to be linked through common geographical area codes to other relevant datasets such as health data. The datasets originate from multiple sources including government bodies, public services, and research datasets. They cover many key categories of wider determinants of health including socioeconomics, employment, housing, support services, and the environment. Key data gaps included food consumption, social care data and community/voluntary services. The data resource profile in this article links directly to freely accessible datasets at the time of publication in the tables provided. The authors welcome contact from researchers for collaboration on exploring wider determinants of health through data use. Please contact Dr Melanie Rees-Roberts m.rees-roberts@kent.ac.uk

A Short term Electricity Load Forecasting for Community Residents Based on Federated Learning and Considering Privacy Protection

INTRODUCTION: As the penetration rate of renewable energy increases and patterns of energy demand evolve, fluctuations on both the supply and demand sides of electricity are becoming more pronounced. Consequently, accurate forecasting of community residential electrical loads has become crucial. OBJECTIVES: Although the widespread adoption of smart meters among residents provides abundant data for model training, strict challenges arise during the training process due to the need for privacy protection and data security. METHODS: This paper proposes a privacy-preserving community residential short-term electric load forecasting method based on federated learning. Initially, the method applies shared random masking encryption to the sensitive data of community residents, ensuring data privacy while maintaining consistency with the original data after preprocessing. Subsequently, a private data aggregation scheme is established to perform dynamic clustering of the community’s electrical load. RESULTS: The clustered model then serves as the basis for establishing individual load forecasting models for each category of community residents to predict short-term electrical loads. Finally, an empirical analysis is performed using the electrical load data from 120 households across 6 communities in a city in Southern China. CONCLUSION: The analysis demonstrates that the proposed method can achieve the prediction of community residential electrical loads without sharing residents’ data, thus verifying the effectiveness of this approach.

A-182 Applications of artificial intelligence optical character recognition in laboratory clerical functions offsetting staffing shortages and error reduction

Abstract Background The clinical laboratory profession has been dealing with a decade long, and now critical retention and staffing problem. The average national turnover rate for laboratory technicians is 17%, and the highest turnover rate exhibited for phlebotomists is 36%. While turnover rates have an impact on a laboratory, or hospitals budget, more importantly they have a substantial impact on quality and error rates, particularly in the pre-analytical stage of testing; with studies showing up to 65.09% of errors impacting the pre-analytical phase. Methods In an effort to remedy this high error rate, a meta-analysis was performed on paper order entry and clerical pre-analytical error, in parallel with performance and accuracy data of advanced optical character recognition driven by artificial intelligence and machine learning (AI OCR). Results AI OCR studies demonstrated an untrained accuracy percentage, to include handwriting of 91.08%, and when trained utilizing both relational and hierarchal logic data bases accuracy increased to 98.4%. The rate at which AI OCR processed and classified medical information was 3-4 pages per second. It is estimated that 99.9%-100% accuracy can be achieved with additional form field training and mapping. The level of accuracy and efficiency for laboratory paper order-entry utilizing AI OCR can also be increased with the addition of natural language processing and data recognition algorithms when combined with logic databases containing information such as medical record numbers, ICD-10 codes, and laboratory test compendiums. Conclusions The performance of this technology demonstrates a strong application to reduce the burden of clerical paper order entry, while addressing the 30.8% dissatisfaction rate regarding manual entry processes among lab professionals surveyed. Moreover, AI OCR clerical data classification, extraction and entry processes augmented with human verification steps examined, demonstrated overall accuracy of 99.99%. The application of AI OCR in laboratory pre-analytical processes has the potential to improve test order accuracy and efficiency, ultimately impacting the quality of patient care and operational costs for healthcare facilities. When examined through the lens of staff retention, studies show that 80% of laboratory employees feel that a workplace with the necessary tools and technology positively impacts their job satisfaction. While organizations investing in technologies that drive both job efficiency and satisfaction see a 125% return on investment through productivity. Demonstrating that the application of sophisticated technologies that both increase productivity and decrease error rates have a substantial impact on job satisfaction and ultimately retention. Although laboratory clerical data and order entry processes were examined for this meta-analysis, the aggregate data compiled showed that the application of AI OCR in clerical processes could also have a substantial impact on the 25.9% annual turnover rate for hospitals as a whole in the United States.

Industrial data-driven machine learning soft sensing for optimal operation of etching tools

Smart Manufacturing, or Industry 4.0, has gained significant attention in recent decades with the integration of Internet of Things (IoT) and Information Technologies (IT). As modern production methods continue to increase in complexity, there is a greater need to consider what variables can be physically measured. This advancement necessitates the use of physical sensors to comprehensively and directly gather measurable data on industrial processes; specifically, these sensors gather data that can be recontextualized into new process information. For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. One industry that has a high volume of labor-intensive, time-consuming, and expensive processes is the semiconductor industry. AI machine learning methods can meet these challenges by taking in operational data and extracting process-specific information needed to meet the high product specifications of the industry. However, a key challenge is the availability of high quality data that covers the full operating range, including the day-to-day variance. This paper examines the applicability of soft sensing methods to the operational data of five industrial etching machines. Data is collected from readily accessible and cost-effective physical sensors installed on the tools that manage and control the operating conditions of the tool. The operational data are then used in an intelligent data aggregation approach that increases the scope and robustness for soft sensors in general by creating larger training datasets comprised of high value data with greater operational ranges and process variation. The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. For PASS/FAIL classification, a data aggregation method is developed to enhance model predictive performance with larger training datasets. A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. For large datasets with high quality data that enable model training for more complex tasks, regression models that predict the oxide thickness of the product are also developed. Two types of models with different loss functions are tested to compare the effects of the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) loss functions on model performance. Both the classification and regression models can be applied in industrial settings as they provide additional information regarding the process outcome. Individually, these models can reduce the number of metrology steps in semiconductor factories, and when developed further, can empower the development of advanced process control strategies.

A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data

Location data is frequently collected from populations and shared in aggregate form to guide policy and decision making. However, the prevalence of aggregated data also raises the privacy concern of membership inference attacks (MIAs). MIAs infer whether an individual's data contributed to the aggregate release. Although effective MIAs have been developed for aggregate location data, these require access to an extensive auxiliary dataset of individual traces over the same locations, which are collected from a similar population. This assumption is often impractical given common privacy practices surrounding location data. To measure the risk of an MIA performed by a realistic adversary, we develop the first Zero Auxiliary Knowledge (ZK) MIA on aggregate location data, which eliminates the need for an auxiliary dataset of real individual traces. Instead, we develop a novel synthetic approach, such that suitable synthetic traces are generated from the released aggregate. We also develop methods to correct for bias and noise, to show that our synthetic-based attack is still applicable when privacy mechanisms are applied prior to release. Using two large-scale location datasets, we demonstrate that our ZK MIA matches the state-of-the-art Knock-Knock (KK) MIA across a wide range of settings, including popular implementations of differential privacy (DP) and suppression of small counts. Furthermore, we show that ZK MIA remains highly effective even when the adversary only knows a small fraction (10%) of their target's location history. This demonstrates that effective MIAs can be performed by realistic adversaries, highlighting the need for strong DP protection.

Living systematic review and comprehensive network meta-analysis of ALS clinical trials: study protocol.

Amyotrophic lateral sclerosis (ALS) is a fatal neurogenerative disease with no effective treatment to date. Despite numerous clinical trials, the majority of studies have been futile in their effort to significantly alter the course of the disease. However, these studies may still provide valuable information for identifying patient subgroups and generating new hypotheses for future research. Additionally, synthesising evidence from these studies may help overcome the limitations of individual studies. Network meta-analysis may refine the assessment of efficacy in specific patient subgroups, evaluate intervention characteristics such as mode of administration or biological mechanisms of action, and rank order promising therapeutic areas of interest. Therefore, we aim to synthesise the available evidence from ALS clinical trials. We will conduct a systematic review to identify all clinical trials that assessed disease-modifying pharmaceutical therapies, cell therapies, or supplements in patients with ALS. Outcomes of interest are clinical disease progression outcomes and survival. We will conduct this search in the period Q4 2024 in three databases: PubMed, Embase and ClinicalTrials.gov for studies from 1999 to 2023. Individual patient data and aggregate data will be collected and subsequentially synthesised in meta-analytical models. The final model will be presented as an open-source web application with biannual updates of the underlying data, thereby providing a 'living' overview of the ALS clinical trial landscape. No ethics approvals are required. Findings will be presented at relevant conferences and submitted to peer-reviewed journals. Data will be stored anonymously in secure repositories.

Characteristics and Consequences of Violent Victimization in Sexual and Gender Minority Communities: An Analysis of the 2017-2021 National Crime Victimization Survey.

Purpose: This article investigates rates of violent victimization, subsequent help-seeking, and health-related consequences within sexual and gender minority (SGM) communities. Methods: Aggregate data from the 2017-2021 National Crime Victimization Survey were examined to determine nationally representative estimates of rates and distributions of violent victimization, help-seeking, and socioemotional consequences within those 16 years of age and older. Due to sample size, most analyses aggregated sexual orientation and gender identity to allow comparison of SGM persons to non-SGM persons and examine differences within the SGM population. Results: Persons who identified as lesbian, gay, or bisexual experienced violent victimization at rates two to six times higher than straight persons. Transgender persons were victimized more than three times as often than cisgender persons. SGM persons experienced higher rates of all types of violent victimization than non-SGM persons regardless of victim-offender relationship. There were differences by victim demographic characteristics, including sex, race and Hispanic origin, age, marital status, and household income. A higher proportion of SGM victims reported only problems with work/school or problems both at work/school and with family/friends. Finally, higher proportions of SGM victims reported socioemotional consequences when they were female, older, or experienced serious violent crime. Conclusion: The findings in this study continue to highlight high levels of violence experienced by SGM persons and disproportionate socioemotional consequences. There is an evident need to develop targeted interventions and provide services to address the consequences of victimization among this population. The analyses demonstrate the necessity of continued research to better understand the impact of violence on SGM communities.

Is There a Stable Relationship between Unemployment and Future Inflation?

Evaluating the stability of the Phillips curve using aggregate data is challenging due to the bias that endogenous monetary policy imparts on estimated Phillips curve coefficients. We argue that regional data can be used to identify the structural relationship between unemployment and inflation. Our analysis, using city- and state-level data from 1977 to 2017, is consistent with the notion that both the reduced-form and the structural parameters of the Phillips curve are, to a substantial degree, quite stable. (JEL E12, E24, E31, E52, E58)

Comprehensive Proteomic Analysis of Dysferlinopathy Unveiling Molecular Mechanisms and Biomarkers Linked to Pathological Progression.

Previous proteomics studies in dysferlinopathy muscle have been limited in scope, often utilizing 2D-electrophoresis and yielding only a small number of differential expression calls. To address this gap, this study aimed to employ high-resolution proteomics to explore the proteomic landscapes of dysferlinopathy and analyze the correlation between muscle pathological changes and alterations in protein expression in muscle biopsies. We conducted a comprehensive approach to investigate the proteomic profile and disease-associated changes in the muscle tissue proteome from 15 patients with dysferlinopathy, exhibiting varying degrees of dystrophic pathology, alongside age-matched controls. Our methodology encompasses tandem mass tag (TMT)-labeled liquid chromatography-mass spectrometry (LC-MS/MS)-based proteomics, protein-protein interaction (PPI) network analysis, weighted gene co-expression network analysis, and differential expression analysis. Subsequently, we examined the correlation between the expression of key proteins and the clinical characteristics of the patients to identify pathogenic targets associated with DYSF mutations in dysferlinopathy. A total of 1600 differentially expressed proteins were identified, with 1321 showing high expression levels and 279 expressed at lower levels. Our investigation yields a molecular profile delineating the altered protein networks in dysferlinopathy-afflicted skeletal muscle, uncovering dysregulation across numerous cellular pathways and molecular processes, including mRNA metabolic processes, regulated exocytosis, immune response, muscle system processes, energy metabolic processes, and calcium transmembrane transport. Moreover, we observe significant associations between the protein expression of ANXA1, ANXA2, ANXA4, ANXA5, LMNA, PYGM, and the extent of histopathologic changes in muscle biopsies from patients with dysferlinopathy, validated through immunoblotting and immunofluorescence assays. Through the aggregation of expression data from dysferlinopathy-impacted muscles exhibiting a range of pathological alterations, we identified multiple key proteins associated with the dystrophic pathology of patients with dysferlinopathy. These findings provide novel insights into the pathogenesis of dysferlinopathy and propose promising targets for future therapeutic endeavors.

Artificial intelligence and informatics in neonatal resuscitation

Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be enabled by leveraging informatics tools and artificial intelligence. Despite technological advancements, these data are often underutilized due to suboptimal data capture, aggregation, and low adoption of artificial intelligence and analytic tools. This review describes the fundamentals and explores the evidence behind informatics and artificial intelligence tools supporting neonatal intensive care unit resuscitative care, training and education. Key findings include the need for effective interface design for accurate data capture followed by storage and translation to wisdom using analytics and artificial intelligence tools. This review addresses the issues of data privacy, bias, liability and ethical frameworks when adopting these tools. While these emerging technologies hold great promise to improve resuscitation, further study of these applications in neonatal population and awareness of informatics and artificial intelligence principles among clinicians is imperative.

Heterogeneous inflation expectations: A call for customized monetary policy communication?

The extent to which firms differentiate their inflation expectations between one-year and two-year horizons is an important indicator of changes in inflation expectations. Within a monetary policy framework aimed at reducing the inflation rate toward the target band, firms that obtain information from the central bank are more likely to distinguish between these horizons and expect inflation to converge toward the target. Generally, decision-makers do not differentiate between horizons, but when they do, they are more likely to anticipate convergence. Conversely, external advisors often differentiate between horizons and are more likely to expect divergence. The heterogeneity in how inflation expectations are formed—depending on who within the firm sets these expectations, the information they use, and their use of aggregate inflation expectations data—suggests a need for customized monetary policy communication.

P2.11B.04 Chemotherapy Increases Acquired Resistance to Immunotherapy in NSCLC: A Metanalysis of Aggregate and Individual Patient Data

P2.11B.04 Chemotherapy Increases Acquired Resistance to Immunotherapy in NSCLC: A Metanalysis of Aggregate and Individual Patient Data

Local Differential Privacy for correlated location data release in ITS

The ubiquity of location positioning devices has facilitated the implementation of various Intelligent Transportation System (ITS) applications that generate an enormous volume of location data. Recently, Local Differential Privacy (LDP) has been proposed as a rigorous privacy framework that permits the continuous release of aggregate location statistics without relying on a trusted data curator. However, the conventional LDP was built upon the assumption of independent data, which may not be suitable for inherently correlated location data. This paper investigates the quantification of potential privacy leakage in a correlated location data release scenario under a local setting, which has not been addressed in the literature. Our analysis shows that the privacy guarantee of LDP could be degraded in the presence of spatial–temporal and user correlations, albeit the perturbation is performed locally and independently by the users. This privacy guarantee is bounded by a privacy barrier that is affected by the intensity of correlations. We derive several important closed-form expressions and design efficient algorithms to compute such privacy leakage in a correlated location data. We subsequently propose a Δ-CLDP model that enhances the conventional LDP by incorporating the data correlations, and design a generic LDP data release framework that renders adaptive personalization of privacy preservation. Extensive theoretical analyses and simulations on scalable real datasets validate the security and performance efficiency of our work.

Lightweight Verifiable Privacy-Preserving Data Aggregation for Smart Grids

Lightweight Verifiable Privacy-Preserving Data Aggregation for Smart Grids

Establishing Consensus for Mohs Micrographic Surgical Techniques in the Treatment of Melanoma in Situ for Future Clinical Trials: A Modified Delphi Study.

Mohs micrographic surgery (MMS) is a promising treatment modality for melanoma in situ (MIS). However, variations in surgical technique limit the generalizability of existing data and may impede future study of MMS in clinical trials. A modified Delphi method was selected to establish consensus on optimal MMS techniques for treating MIS in future clinical trials. The Delphi method was selected due to the limited current data, the wide range of techniques used in the field, and the intention to establish a standardized technique for future clinical trials. A literature review and interviews with experienced MMS surgeons were performed to identify dimensions of the MMS technique for MIS that (1) likely impacted costs or outcomes of the procedure, and (2) showed significant variability between surgeons. A total of 8 dimensions of technical variation were selected. The Delphi process consisted of 2 rounds of voting and commentary, during which 44 expert Mohs surgeons across the United States rated their agreement with specific recommendations using a Likert scale. Five of eight recommendations achieved consensus in Round 1. All 3 of the remaining recommendations achieved consensus in Round 2. Techniques achieving consensus in Round 1 included the use of a starting peripheral margin of ≤5 mm, application of immunohistochemistry, frozen tissue processing, and resecting to the depth of subcutaneous fat. Consensus on the use of Wood's lamp, dermatoscope, and negative tissue controls was established in Round 2. This study generated 8 consensus recommendations intended to offer guidance for Mohs surgeons treating MIS. The adoption of these recommendations will promote standardization to facilitate comparisons of aggregate data in multicenter clinical trials.

Analyzing the seasonality of tuberculosis case notifications in the UK, 2000-2018.

Globally, there is seasonal variation in tuberculosis (TB) incidence, yet the biological and behavioural or social factors driving TB seasonality differ across countries. Understanding season-specific risk factors that may be specific to the UK could help shape future decision-making for TB control. We conducted a time-series analysis using data from 152,424 UK TB notifications between 2000 and 2018. Notifications were aggregated by year, month, and socio-demographic covariates, and negative binomial regression models fitted to the aggregate data. For each covariate, we calculated the size of the seasonal effect as the incidence risk ratio (IRR) for the peak versus the trough months within the year and the timing of the peak, whilst accounting for the overall trend. There was strong evidence for seasonality (p < 0.0001) with an IRR of 1.27 (95% CI 1.23-1.30). The peak was estimated to occur at the beginning of May. Significant differences in seasonal amplitude were identified across age groups, ethnicity, site of disease, latitude and, for those born abroad, time since entry to the UK. The smaller amplitude in older adults, and greater amplitude among South Asians and people who recently entered the UK may indicate the role of latent TB reactivation and vitamin D deficiency in driving seasonality.

DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS

The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.

The GENESIS database and tools: A decade of discovery in Mendelian genomics

In the past decade, human genetics research saw an acceleration of disease gene discovery and further dissection of the genetic architectures of many disorders. Much of this progress was enabled via data aggregation projects, collaborative data sharing among researchers, and the adoption of sophisticated and standardized bioinformatics analyses pipelines. In 2012, we launched the GENESIS platform, formerly known as GEM.app, with the aims to 1) empower clinical and basic researchers without bioinformatics expertise to analyze and explore genome level data and 2) facilitate the detection of novel pathogenic variation and novel disease genes by leveraging data aggregation and genetic matchmaking. The GENESIS database has grown to over 20,000 datasets from rare disease patients, which were provided by multiple academic research consortia and many individual investigators. Some of the largest global collections of genome-level data are available for Charcot-Marie-Tooth disease, hereditary spastic paraplegia, and cerebellar ataxia. A number of rare disease consortia and networks are archiving their data in this database. Over the past decade, more than 1500 scientists have registered and used this resource and published over 200 papers on gene and variant identifications, which garnered >6000 citations. GENESIS has supported >100 gene discoveries and contributed to approximately half of all gene identifications in the fields of inherited peripheral neuropathies and spastic paraplegia in this time frame. Many diagnostic odysseys of rare disease patients have been resolved. The concept of genomes-to-therapy has borne out for a number of such discoveries that let to rapid clinical trials and expedited natural history studies. This marks GENESIS as one of the most impactful data aggregation initiatives in rare monogenic diseases.

Data Quality Based Intelligent Instrument Selection with Security Integration

We propose a novel Data Quality with Security (DQS) integrated instrumentation selection approach that facilitates aggregation of multi-modal data from heterogeneous sources. As our major contribution, we develop a framework that incorporates multiple levels of integration in finding the best DQS-based instrument selection: data fusion from multi-modal sensors embedded into heterogeneous platforms, using multiple quality and security metrics and knowledge integration. Our design addresses the security aspect in the instrumentation design, which is commonly overlooked in real applications, by aggregating it with other metrics into an integral DQS calculus. We develop DQS calculus that formalizes the problem of finding the optimal DQS value. We then propose a Genetic Algorithm–based solution to find an optimal set of sensors in terms of the DQS they provide, while maintaining the level of platform security desirable by the user. We show that our proposed algorithm demonstrates optimal real-time performance in multi-platform instrument selection. To facilitate the framework application by the instrumentation designers and users, we develop and make available multiple Android applications.