Biomedical Big Data Research Articles

Biomedical ontologies and their development, management, and applications in and beyond China

Abstract Since the boom of biomedical big data studies, various big data processing technologies have been developed rapidly. As an important form of knowledge representation, ontology has become an important means for the utilization and integration of biomedical big data. The emergence of new technologies for ontology development has resulted in the generation of many biomedical ontologies by many ontology development communities. The Open Biological and Biomedical Ontology Foundry, an academic organization for bio-ontology developers, has provided a set of principles to guide community-based open ontology construction. The Open Biological and Biomedical Ontology Foundry have also built many widely used ontologies, such as Gene Ontology, Human Phenotype Ontology, and Chemical Entities of Biological Interest. Other various ontology repositories have also been created and used to support ontology reuse. Many efficient tools for ontology applications, such as data annotation and terms mapping, have also been developed. High quality ontologies are also being used to develop new methods and tools for biomedical data analysis. The applications of Gene Ontology and Human Phenotype Ontology for data analysis and integration in recent years are reviewed here. To promote the development and applications of biomedical ontologies in China, a research community, OntoChina, was founded recently. OntoChina aims to support the development of reference ontologies, especially bilingual and Chinese translated ontologies. OntoChina also encourages ontology developers to follow the Open Biological and Biomedical Ontology Foundry principles.

CUDA parallel programming technology application for analysis of big biomedical data based on computation of effectiveness features

This paper proposes the technology for large biomedical data analysis based on CUDA computation. The technology was used to analyze a large set of fundus images used for diabetic retinopathy automatic diagnostics. A high-performance algorithm that calculates effective textural characteristics for medical image analysis has been developed. During the automatic image diagnostics, the following classes were distinguished: thin vessels, thick vessels, exudates and a healthy area. The study of the mentioned algorithm efficiency was conducted with 500x500-1000x1000 pixels images using a square 12x12 dimension window. The acceleration relationship between the developed algorithm and various data sizes was demonstrated. The study showed that the algorithm effectiveness can be affected by certain characteristics of the image, e.g. its clarity, shape of exudate zone, variability of blood vessels, and optic disc location.

Big Data and Public-Private Partnerships in Healthcare and Research

Public-private partnerships (PPPs) are established to specifically harness the potential of Big Data in healthcare and can include partners working across the data chain—producing health data, analysing data, using research results or creating value from data. This domain paper will illustrate the challenges that arise when partners from the public and private sector collaborate to share, analyse and use biomedical Big Data. We discuss three specific challenges for PPPs: working within the social licence, public antipathy to the commercialisation of public sector health data, and questions of ownership, both of the data and any resulting intellectual property or products. As a specific example we consider the case of the UK National Health Service (NHS) providing patient data to Google’s DeepMind AI program to develop a diagnostic app for kidney disease. This article is an application of the framework presented in this issue of ABR (Xafis et al. 2019). Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. We use four specific values to help analysis these issues: public benefit, stewardship, transparency and engagement. We demonstrate how the Deliberative Framework can support ethical governance of PPPs involving biomedical big data.

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses.

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

MotivationAutomated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist’s prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data.ResultsWe introduce two new features implemented in TPOT that helps increase the system’s scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT’s efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual.Availability and implementationDetailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss. Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot.Supplementary information Supplementary data are available at Bioinformatics online.

Can dynamic consent facilitate the protection of biomedical big data in biobanking in Malaysia?

As with many other countries, Malaysia is also developing and promoting biomedical research to increase the understanding of human diseases and possible interventions. To facilitate this development, there is a significant growth of biobanks in the country to ensure continuous collection of biological samples for future research, which contain extremely important personal information and health data of the participants involved. Given the vast amount of samples and data accumulated by biobanks, they can be considered as reservoirs of precious biomedical big data. It is therefore imperative for biobanks to have in place regulatory measures to ensure ethical use of the biomedical big data. Malaysia has yet to introduce specific legislation for the field of biobanking. However, it can be argued that its existing Personal Data Protection Act 2010 (PDPA) has laid down legal principles that can be enforced to protect biomedical big data generated by the biobanks. Consent is a mechanism to enable data subjects to exercise their autonomy by determining how their data can be used and ensure compliance with legal principles. However, there are two main concerns surrounding the current practice of consent in biomedical big data in Malaysia. First, it is uncertain that the current practice would be able to respect the underlying notion of autonomy, and second, it is not in accordance with the legal principles of the PDPA. Scholars have deliberated on different strategies of informed consent, and a more interactive approach has recently been introduced: dynamic consent. It is argued that a dynamic consent approach would be able to address these concerns.

The Ethics of Biomedical Big Data

The Ethics of Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data

But for one flaw in the first printing of this text (to be detailed at the end of this review), this reviewer highly recommends this reference text. This 28-chapter, 89-author, large-format ( $8.5\times11$ ”), liberally illustrated (216 b/w figures), well-referenced and up-to-date text will be of value to those entering the field and likely to those interested in teaching various aspects of biomedical big data collection and analysis.

Reproducible big data science: A case study in continuous FAIRness.

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility—thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes.

Network Medicine in the Age of Biomedical Big Data.

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

A Review of the Challenges of Using Biomedical Big Data for Economic Evaluations of Precision Medicine

There is potential value in incorporating biomedical big data (BBD)—observational real-world patient-level genomic and clinical data in multiple sub-populations—into economic evaluations of precision medicine. However, health economists face practical and methodological challenges when using BBD in this context. We conducted a literature review to identify and summarise these challenges. Relevant articles were identified in MEDLINE, EMBASE, EconLit, University of York Centre for Reviews and Dissemination and Cochrane Library from 2000 to 2018. Articles were included if they studied issues relevant to the interconnectedness of biomedical big data, precision medicine, and health economic evaluation. Nineteen articles were included in the review. Challenges identified related to data management, data quality and data analysis. The availability of large volumes of data from multiple sources, the need to conduct data linkages within an environment of opaque data access and sharing procedures, and other data management challenges are primarily practical and may not be long-term obstacles if procedures for data sharing and access are improved. However, the existence of missing data across linked datasets, the need to accommodate dynamic data, and other data quality and analysis challenges may require an evolution in economic evaluation methods. Health economists face challenges when using BBD in economic evaluations of technologies that facilitate precision medicine. Potential solutions to some of these challenges do, however, exist. Going forward, health economists who present work that uses BBD should document challenges and the solutions they have applied to the challenges to support future researcher endeavours.Electronic supplementary materialThe online version of this article (10.1007/s40258-019-00474-7) contains supplementary material, which is available to authorized users.

Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis.

In biomedical domain, abbreviations are appearing more and more frequently in various data sets, which has caused significant obstacles to biomedical big data analysis. The dictionary-based approach has been adopted to process abbreviations, but it cannot handle ad hoc abbreviations, and it is impossible to cover all abbreviations. To overcome these drawbacks, this paper proposes an automatic abbreviation expansion method called LMAAE (Language Model-based Automatic Abbreviation Expansion). In this method, the abbreviation is firstly divided into blocks; then, expansion candidates are generated by restoring each block; and finally, the expansion candidates are filtered and clustered to acquire the final expansion result according to the language model and clustering method. Through restrict the abbreviation to prefix abbreviation, the search space of expansion is reduced sharply. And then, the search space is continuous reduced by restrained the effective and the length of the partition. In order to validate the effective of the method, two types of experiments are designed. For standard abbreviations, the expansion results include most of the expansion in dictionary. Therefore, it has a high precision. For ad hoc abbreviations, the precisions of schema matching, knowledge fusion are increased by using this method to handle the abbreviations. Although the recall for standard abbreviation needs to be improved, but this does not affect the good complement effect for the dictionary method.

Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network.

Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.

보건의료 빅데이터의 윤리적 활용을 위한 방안모색: 동의가 아닌 합의 모델로의 전환*,**

The purpose of this paper is to examine how biomedical big data might be used in an ethically appropriate way. Biomedical big data is currently expected to create tremendous economic value as well as social value. For this reason there is an active debate among biomedical ethicists about which model of consent is most appropriate and efficient in this context. At first glance, the model of dynamic consent seems to be both justifiable and feasible, not only because it is based on the spirit of informed consent, but also because it allows for an additional process of consent through on-line platforms. However, it is doubtful that this model could be used in the context of biomedical big data since it is vulnerable to privacy protection and insensitive to ownership issues. Therefore, I argue that the most appropriate model of consent is an agreement model, which can reflect a fair distribution of the benefits from the use of biomedical big data and the privacy risk management. This agreement model not only promotes the autonomy of information givers, but also facilitates the long-term sustainable use of biomedical big data.

Preparing next-generation scientists for biomedical big data: artificial intelligence approaches.

Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data arebeing stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Big Data, precision medicine and private insurance: A delicate balancing act

In this paper, we discuss how access to health-related data by private insurers, other than affecting the interests of prospective policy-holders, can also influence their propensity to make personal data available for research purposes. We take the case of national precision medicine initiatives as an illustrative example of this possible tendency. Precision medicine pools together unprecedented amounts of genetic as well as phenotypic data. The possibility that private insurers could claim access to such rapidly accumulating biomedical Big Data or to health-related information derived from it would discourage people from enrolling in precision medicine studies. Should that be the case, the economic value of personal data for the insurance industry would end up affecting the public value of data as a scientific resource. In what follows we articulate three principles – trustworthiness, openness and evidence – to address this problem and tame its potentially harmful effects on the development of precision medicine and, more generally, on the advancement of medical science.

Impact of big data on oral health outcomes.

Biomedical big data amasses from different sources such as electronic health records, health research, wearable devices and social media. Recent advances in data capturing, storage and analysis techniques have facilitated conversion of a wealth of knowledge in biomedical big data into evidence-based actionable plans to enhance population health and well-being. The delay in reaping the benefits of biomedical big data in dentistry is mainly due to the slow adoption of electronic health record systems, unstructured clinical records, tattered communication between data silos and perceiving oral health as a separate entity from general health. Recent recognition of the complex interplay between oral and general health has acknowledged the power of oral health big data to glean new insights on disease prevention and management. This review paper summarizes recent advances, limitations and challenges in biomedical big data in health care with emphasis on oral health and discusses the potential future applications of oral health big data to improve the quality and efficiency of personalized health care.

Better Patient Outcomes Through Mining of Biomedical Big Data

Digitalization is changing healthcare today. Big data analytics of medical information allows diagnostics, therapy and development of personalized medicines, to provide unprecedented treatment. This leads to better patient outcomes, while containing costs. In this review, opportunities, challenges and solutions for this health-data revolution are discussed. Integration and near-instant-response analytics across large datasets ¬can support care-givers and researchers to test and discard hypotheses more quickly. Physicians want to compare a patient to other similar patients, to learn and communicate about treatment best-practices with peers, across large cohorts and sets of parameters. Real-time interactions between physician and patient are becoming more important, allowing ‘live’ support of patients instead of single interactions once every few weeks. Researchers from many disciplines (biomedical, payers, governments) want to interpret large anonymized datasets, to uncover trends in drug-candidate behavior, treatment regimens, clinical trials or reimbursements, and to act on those insights. These opportunities are however met by daunting challenges. Biomedical information is available in data silos of structured and unstructured formats (doctor letters, patient records, omics data, device data). Efficient usage of biomedical information is also hampered by data privacy concerns. This has led to a highly-regulated industry, as a result of which digitalization in healthcare has progressed slower than in other industries. This review concludes with examples of how integration and interpretation of big data can be used to break down data silos and pave the way to better patient outcomes, value-based care, and the creation of an intelligent enterprise for healthcare.

From biomedical big data to knowledge and action

From biomedical big data to knowledge and action