scholarly journals Evaluation of a large-scale biomedical data annotation initiative

2009 ◽  
Vol 10 (S9) ◽  
Author(s):  
Ronilda Lacson ◽  
Erik Pitzer ◽  
Christian Hinske ◽  
Pedro Galante ◽  
Lucila Ohno-Machado
Keyword(s):  
Large Scale ◽  
Biomedical Data ◽  
Data Annotation

scholarly journals Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Andreas Friedrich ◽  
Erhan Kenar ◽  
Oliver Kohlbacher ◽  
Sven Nahnsen
Keyword(s):  
Big Data ◽  
Experimental Design ◽  
High Throughput ◽  
Large Scale ◽  
Integrated Design ◽  
Added Value ◽  
Biomedical Data ◽  
Data Generation ◽  
Web Based ◽  
Data Annotation

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

scholarly journals Architectural concepts for managing biomedical sensor data utilised for medical diagnosis and patient remote care.

2018 ◽  
Vol 210 ◽  
pp. 05016
Author(s):  
Mariusz Chmielewski ◽  
Damian Frąszczak ◽  
Dawid Bugajewski
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Large Scale ◽  
Data Representation ◽  
Medical Diagnostics ◽  
Mobile Technologies ◽  
Sensor Data ◽  
Biomedical Data ◽  
Support Tool ◽  
Decision Algorithms

This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.

scholarly journals A Medical Informatics Perspective on Health Informatics 3.0

2011 ◽  
Vol 20 (01) ◽  
pp. 30-32
Author(s):  
P. Ruch ◽  
Keyword(s):  
Semantic Web ◽  
Health Informatics ◽  
Large Scale ◽  
Biomedical Data ◽  
Biomedical Knowledge ◽  
Data Set ◽  
Web 3.0 ◽  
Research Fields ◽  
Emerging Trends ◽  
Excellent Research

SummaryTo summarize current advances of the so-called Web 3.0 and emerging trends of the semantic web.We provide a synopsis of the articles selected for the IMIA Yearbook 2011, from which we attempt to derive a synthetic overview of the today’s and future activities in the field.while the state of the research in the field is illustrated by a set of fairly heterogeneous studies, it is possible to identify significant clusters. While the most salient challenge and obsessional target of the semantic web remains its ambition to simply interconnect all available information, it is interesting to observe the developments of complementary research fields such as information sciences and text analytics. The combined expression power and virtually unlimited data aggregation skills of Web 3.0 technologies make it a disruptive instrument to discover new biomedical knowledge. In parallel, such an unprecedented situation creates new threats for patients participating in large-scale genetic studies as Wjst demonstrate how various data set can be coupled to re-identify anonymous genetic information.The best paper selection of articles on decision support shows examples of excellent research on methods concerning original development of core semantic web techniques as well as transdisciplinary achievements as exemplified with literature-based analytics. This selected set of scientific investigations also demonstrates the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge with potential threats for confidentiality directly associated with such advances. Altogether these papers support the idea that more elaborated computer tools, likely to combine heterogeneous text and data contents should soon emerge for the benefit of both experimentalists and hopefully clinicians.

scholarly journals Opportunities for Artificial Intelligence in Advancing Precision Medicine

2019 ◽  
Vol 7 (4) ◽  
pp. 208-213 ◽  
Author(s):  
Fabian V. Filipp
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Large Scale ◽  
Digital Health ◽  
Disease Classification ◽  
Biomedical Data ◽  
Cell Clustering ◽  
Genome Wide ◽  
Definition Of ◽  
Disease Signatures

Abstract Purpose of Review We critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. Recent Findings High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Summary Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.

scholarly journals Infectious Disease Research in the Era of Big Data

2020 ◽  
Vol 3 (1) ◽  
pp. 43-59
Author(s):  
Peter M. Kasson
Keyword(s):  
Infectious Disease ◽  
Large Scale ◽  
Data Science ◽  
Research Progress ◽  
Biomedical Data ◽  
Disease Research ◽  
Research Areas ◽  
Large Scale Data ◽  
Infectious Disease Research ◽  
Unique Nature

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

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