Creating a platform for collaborative genomic research

Selection of genes that are differentially expressed and critical to a particular biological process has been a major challenge in post-array analysis. Recent development in bioinformatics has made various data sources available such as mRNA and miRNA expression profiles, biological pathway and gene annotation, etc. Efficient and effective integration of multiple data sources helps enrich our knowledge about the involved samples and genes for selecting genes bearing significant biological relevance. In this work, we studied a novel problem of multi-source gene selection: given multiple heterogeneous data sources (or data sets), select genes from expression profiles by integrating information from various data sources. We investigated how to effectively employ information contained in multiple data sources to extract an intrinsic global geometric pattern and use it in covariance analysis for gene selection. We designed and conducted experiments to systematically compare the proposed approach with representative methods in terms of statistical and biological significance, and showed the efficacy and potential of the proposed approach with promising findings.

In recent years, huge different biological databases have been stored in various locations. Using distinct data sets from multiple sources results in more reliable data analysis. However, it is so difficult to combine heterogeneous data in one single server. The most obvious reasons include data privacy, large data sizes, costs, and different geographical locations of data sources. In this paper, we present two new algorithms for clustering data from multiple remote data sources using kernel k-means. The first algorithm is the center-based algorithm built on k-means algorithm. The second algorithm uses distributed kernel k-means over multiple data sources. In the distributed scheme, clustering methods are executed only on their local data sources themselves. Partial clustering results are synched between data sources. To evaluate performance of our proposed algorithms, we merged all data from different sources into one large data set to perform kernel k-means. The results showed that our center-based algorithm greatly reduced transmission data between data sources while still yielding acceptable clustering results. Our distributed kernel k-means algorithm achieved even better performance. The clustering results are very close to those generated by kernel k-means on one merged data set.

It is challenging to identify meaningful gene networks because biological interactions are often condition-specific and confounded with external factors. It is necessary to integrate multiple sources of genomic data to facilitate network inference. For example, one can jointly model expression datasets measured from multiple tissues with molecular marker data in so-called genetical genomic studies. In this paper, we propose a joint conditional Gaussian graphical model (JCGGM) that aims for modeling biological processes based on multiple sources of data. This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization. We apply our approach to a real dataset measuring gene expression in four tissues (kidney, liver, heart, and fat) from recombinant inbred rats. Our approach reveals that the liver tissue has the highest level of tissue-specific gene regulations among genes involved in insulin responsive facilitative sugar transporter mediated glucose transport pathway, followed by heart and fat tissues, and this finding can only be attained from our JCGGM approach.

Alpha Credit Card Company Limited (hereafter ACCCL) which was engaged in unauthorized fund collecting from the Sri Lankan public, collapsed in year 2008. Analysis of ACCCL’s multiple sources of archival data found reason being inefficiency of ACCCL’s corporate governance system. Three research questions describe the field “corporate governance structure of ACCCL” were addressed. Thereafter analysed how prevailed governance failures contributed to collapse of ACCCL. Aim of this paper is to explain why the company failed, and will discuss question “governance structure prevailed at ACCCL?” and objective “salient features of governance structure prevailed at ACCCL. Access to corporate data is the key to case study success (Starkey, 1997). Prevailed scandalous environment embedded within the company resulted ACCCL ranked among the largest frauds committed in South Asia. This setup prevailed grounds for my analytical study as unique field with available resources to analyse the case. Despite difficulty to access data directly from company, multiple sources of data were used. Findings were internal, external corporate governance failures where both information demand and supply side actors, failed to monitor ACCCL’s activities. Keywords: Case Study, ACCCL, Corporate Governance failures, Scandal.For full Paper: fmscresearch@sjp.ac.lk

Research in suicide risk prediction often suffers from the lack of comprehensive data on patient suicide death, which differs from suicide attempt or suicidal behaviors. This study aimed to develop a population-wide multi-source harmonized data warehouse suitable for suicide death risk prediction. The Maryland Suicide Data Warehouse (MSDW) was conceived as a statewide database that addresses limitations in prior suicide research. To develop MSDW, multiple patient-level statewide data sources were linked using the statewide health information exchange infrastructure. Manner of death, the standard outcome in suicide death research, was determined by the state medical examiner. Health services data were linked from multiple data sources such as electronic health records, hospital discharge data, and administrative insurance claims. Data were structured as a common format that preserves observations at their lowest level of analysis. Data features were included based on known or hypothesized psychiatric or suicide risk factors. The warehouse contains a mix of records across data sources for patient diagnoses, clinical encounters, procedures, area of residence, pharmacy fills and laboratory findings. MSDW represents 104,517 decedents reported by the OCME between 2012 and 2020, 5,059 classified as suicides. The MSDW is a statewide data warehouse that allows users to conduct population health research, predictive modeling and observational studies for multiple outcomes. It has multiple overlapping clinical records that improve the completeness and timeliness of data. It is a high-quality statewide data warehouse for conducting suicide prediction research and assessing risk for surveillance and intervention.

Detecting functional modules from protein-protein interaction (PPI) networks is an active research area with many practical applications. However, there is always a critical concern on the false PPI interactions which are derived from the high-throughput experiments and the unsatisfactory results obtained from single PPI network with severe information insufficiency. To address this problem, we propose a Collective Non-negative Matrix Factorization (CoNMF) based soft clustering method which efficiently integrates information of gene ontology (GO), gene expression data and PPI networks. In our method, the three data sources are formed into two graphs with similarity adjacency matrices and these graphs are approximated by a matrix factorization with their common factor which provides the straight-forward interpretation of clustering results. Extensive experiments show that we can improve the module detection performance by integrating multiple biological data sources and that CoNMF yields superior results compared to other multiple data sources fusion methods by identifying a larger number of more precise protein modules with actual biological meaning and certain degree of overlapping.

BackgroundReal-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through harmonization.MethodsThis paper’s aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN; FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform’s strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)–based program. FOUNTAIN’s overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its life-cycle.ResultsCurrently, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase, cohort studies are ongoing to describe patient populations, current standard of care, comorbidity profiles, healthcare resource use, and treatment effectiveness and safety in different patient populations with chronic kidney disease and type 2 diabetes. Strengths and potential limitations of FOUNTAIN are described in the context of valid RWE generation.ConclusionThe establishment of the FOUNTAIN platform has allowed harmonized execution of multiple studies, promoting consistency both within individual studies that employ multiple data sources and across all studies run within the platform’s framework. FOUNTAIN presents a proposal to efficiently improve the consistency and generalizability of RWE on finerenone.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Personalized treatments and targeted therapies are the most promising approaches to treat complex diseases, especially for cancer. However, drug resistance is often acquired after treatments. To overcome or reduce drug resistance, treatments using drug combinations have been actively investigated in the literature. Existing methods mainly focus on chemical properties of drugs for potential combination therapies without considering relationships among different diseases. Also, they often do not consider the rich knowledge of drugs and diseases, which can enhance the prediction of drug combinations. This motivates us to develop a new computational method that can predict the beneficial drug combinations. We propose DrugCom, a tensor-based framework for computing drug combinations across different diseases by integrating multiple heterogeneous data sources of drugs and diseases. DrugCom first constructs a primary third-order tensor (i.e., drug×drug ×disease) and several similarity matrices from multiple data sources regarding drugs (e.g., chemical structure) and diseases (e.g., disease phenotype). DrugCom then formulates an objective function, which simultaneously factorizes coupled tensor and matrices to reveal the molecular mechanisms of drug synergy. We adopt the alternating direction method of multipliers algorithm to effectively solve the optimization problem. Extensive experimental studies using real-world datasets demonstrate superior performance of DrugCom.

This education-focused paper presents a discussion of possible data sources used in patient safety issues specific to fall reduction in hospital inpatient care settings. Although hospitals and clinicians in the USA have been implored to improve care and reduce events that harm patients (falls), studies to date have failed to clearly address the facility system-level factors for falls. Making meaningful approaches to modify risk factors is clearly overdue. Discursive paper. Possible data sources for answering patient fall-related research questions in hospital settings are categorised as: (1) archived hospital data, (2) surveys of patients/families/clinicians, (3) interviews and focus groups of patients/families/clinicians, (4) publicly available data sets and (5) published legal cases. The complexities of research in fall prevention are illustrated using the conceptual models. Examples were included to illustrate the use of these data sources. Data-related issues include: (1) unit of analysis, (2) computer data processing capabilities, (3) merging data sets from different sources and (4) data abstraction, aggregation and data analytic techniques. The trend to use multiple data sources to answer research questions is gradually emerging. To demonstrate effective fall prevention efforts across hospitals, publicly available data sets can be reliable sources for analyses to inform policymakers about meaningful fall prevention programmes that result in positive outcomes. Challenges to develop and evaluate any interventions to eliminate risk factors for falls often relate to selecting feasible interventions and whether staff members accept the interventions and adhere to adopting the intervention. Using multiple data sources with time factors to cross-validate the sufficiency of nurses' knowledge with their practice patterns may be more productive. This need further supports the importance of this paper about possible data sources used in the research on patient safety specific to fall reduction for adults in hospital inpatient care settings.

Detecting genetic variants for extreme aging using multiple data sources

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQLconstructs required to benefit from this data - in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.

ABSTRACTObjective: Traffic incidents occurring on roadways require the coordinated effort of multiple responder and recovery entities, including communications, law enforcement, fire and rescue, emergency medical services, hazardous materials, transportation agencies, and towing and recovery. The objectives of this study were to (1) identify and characterize transportation incident management (TIM)-related occupational fatalities; (2) assess concordance of surveillance data sources in identifying TIM occupations, driver vs. pedestrian status, and occupational fatality incident location; and (3) determine and compare U.S. occupational fatality rates for TIM industries.Methods: The Kentucky Fatality Assessment and Control Evaluation (FACE) program analyzed 2005–2016 TIM occupational fatality data using multiple data sources: death certificate data, Collision Report Analysis for Safer Highways (CRASH) data, and media reports, among others. Literal text analysis was performed on FACE data, and a multiple linear regression model and SAS proc sgpanel were used to estimate and visualize the U.S. TIM occupational mortality trend lines and confidence bounds.Results: There were 29 TIM fatalities from 2005 to 2015 in Kentucky; 41% of decedents were in the police protection occupation, and 21% each were in the fire protection and motor vehicle towing industries. Over one half of the TIM decedents were performing work activities as pedestrians when they died. Media reports identified the majority of the occupational fatalities as TIM related (28 of 29 TIM-related deaths); the use of death certificates as the sole surveillance data source only identified 17 of the 29 deaths as TIM related, and the use of CRASH data only identified 4 of the 29 deaths as TIM related. Injury scenario text analysis showed that law enforcement vehicle pursuit, towing and recovery vehicle loading, and disabled vehicle response were particular high-risk activities that led to TIM deaths. Using U.S. data, the motor vehicle towing industry had a significantly higher risk for occupational mortality compared to the fire protection and police protection industries.Conclusions: Multiple data sources are needed to comprehensively identify TIM fatalities and to examine the circumstances surrounding TIM fatalities, because no one data source in itself was adequate and undercounted the total number of TIM fatalities. The motor vehicle towing industry, in particular, is at elevated risk for occupational mortality, and targeted mandatory TIM training for the motor vehicle towing industry should be considered. In addition, enhanced law enforcement roadside safety training during vehicle pursuit and apprehension of suspects is recommended.

An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning

The hypertext visionaries foresaw the potential of richly interlinked global information systems for advancing human knowledge. The Web provided the infrastructure to enable those ideas to become a reality, and it quickly became a platform for collaborative research and data sharing. As the Web has evolved, new ways of using it for eResearch have emerged, such as the social networking facilities enabled by Web 2.0 technologies. The next generation of the Web-the so-called Semantic Web--is now on the horizon, which will again enable new types of collaborative research to emerge. If we are to understand and anticipate these new modes of collaboration, we need a discipline that studies the Web as a whole. Web science is this discipline.