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 Progress Towards and Challenges in Biological Big Data

2018 ◽  
Vol 1 (4) ◽  
pp. 35-38
Author(s):  
Manisha Sritharan ◽  
Farhat A. Avin
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Modern Technology ◽  
Biological Data ◽  
Biomedical Data ◽  
Data Generation ◽  
Huge Amount ◽  
Efficient Manner ◽  
Meaningful Information ◽  
Research Pattern

Biological big data represents a vast amount of data in bioinformatics and this could lead to the transformation of the research pattern into large scale. In medical research, a large amount of data can be generated from tools including genomic sequencing machines. The availability of advanced tools and modern technology has become the main reason for the expansion of biological data in a huge amount. Such immense data should be utilized in an efficient manner in order to distribute this valuable information. Besides that, storing and dealing with those big data has become a great challenge as the data generation are tremendously increasing over years. As well, the blast of data in healthcare systems and biomedical research appeal for an immediate solution as health care requires a compact integration of biomedical data. Thus, researchers should make use of this available big data for analysis rather than keep creating new data as they could provide meaningful information with the use of current advanced bioinformatics tools.

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

scholarly journals Fast Transient Detection as a Prototypical “Big Data” Problem

2011 ◽  
Vol 7 (S285) ◽  
pp. 340-341
Author(s):  
Dayton L. Jones ◽  
Kiri Wagstaff ◽  
David Thompson ◽  
Larry D'Addario ◽  
Robert Navarro ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
High Time Resolution ◽  
Data Generation ◽  
Time Data ◽  
Transient Detection ◽  
Data Archiving ◽  
Large Scale Data ◽  
Data Problem ◽  
Scale Data

AbstractThe detection of fast (< 1 second) transient signals requires a challenging balance between the need to examine vast quantities of high time-resolution data and the impracticality of storing all the data for later analysis. This is the epitome of a “big data” issue—far more data will be produced by next generation-astronomy facilities than can be analyzed, distributed, or archived using traditional methods. JPL is developing technologies to deal with “big data” problems from initial data generation through real-time data triage algorithms to large-scale data archiving and mining. Although most current work is focused on the needs of large radio arrays, the technologies involved are widely applicable in other areas.

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 The PCR Simulator: an on-line application for teaching Design of Experiments and the polymerase chain reaction

2018 ◽  
Author(s):  
Harold Fellermann ◽  
Ben Shirt-Ediss ◽  
Jerzy W. Koryza ◽  
Matthew Linsley ◽  
Dennis W. Lendrem ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Experimental Design ◽  
Data Generation ◽  
Teaching Session ◽  
Design Strategies ◽  
Factorial Experimental Design ◽  
Chain Reaction ◽  
Web Based ◽  
On Line ◽  
Polymerase Chain

Our PCR Simulator is a web-based application designed to introduce concepts of multi-factorial experimental design and support teaching of the polymerase chain reaction. Learners select experimental settings and receive results of their simulated reactions quickly, allowing rapid iteration between data generation and analysis. This enables the student to perform complex iterative experimental design strategies within a short teaching session. Here we provide a short overview of the user interface and underpinning model, and describe our experience using this tool in a teaching environment.

An Analysis of Big Data Analytics

2021 ◽  
pp. 203-230
Author(s):  
Vijander Singh ◽  
Amit Kumar Bairwa ◽  
Deepak Sinwar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Past Century ◽  
Data Generation ◽  
Microsoft Excel ◽  
Web Based ◽  
The Past ◽  
Digital World ◽  
World Information

In the development of the advanced world, information has been created each second in numerous regions like astronomy, social locales, medical fields, transportation, web-based business, logical research, horticulture, video, and sound download. As per an overview, in 60 seconds, 600+ new clients on YouTube and 7 billion queries are executed on Google. In this way, we can say that the immense measure of organized, unstructured, and semi-organized information are produced each second around the cyber world, which should be managed efficiently. Big data conveys properties such as unpredictability, 'V' factor, multivariable information, and it must be put away, recovered, and dispersed. Logical arranged data may work as information in the field of digital world. In the past century, the sources of data as to size were very limited and could be managed using pen and paper. The next generation of data generation tools include Microsoft Excel, Access, and database tools like SQL, MySQL, and DB2.

scholarly journals Troubleshooting unstable molecules in chemical space

2021 ◽  
Vol 12 (15) ◽  
pp. 5566-5573
Author(s):  
Salini Senthil ◽  
Sabyasachi Chakraborty ◽  
Raghunathan Ramakrishnan
Keyword(s):  
Big Data ◽  
Quantum Chemistry ◽  
High Throughput ◽  
Chemical Space ◽  
Geometry Optimization ◽  
Data Generation ◽  
Structural Rearrangements ◽  
Unstable Molecules

A high-throughput workflow for connectivity preserving geometry optimization minimizes unintended structural rearrangements during quantum chemistry big data generation.

Interference: A Much-Neglected Aspect in High-Throughput Screening of Nanoparticles

2020 ◽  
Vol 39 (5) ◽  
pp. 397-421
Author(s):  
Charlene Andraos ◽  
Il Je Yu ◽  
Mary Gulumian
Keyword(s):  
Big Data ◽  
High Throughput ◽  
High Throughput Screening ◽  
Fluorescent Dyes ◽  
Membrane Integrity ◽  
Data Generation ◽  
Label Free ◽  
Predictive Toxicology ◽  
Toxicity Assay ◽  
Novel Concept

Despite several studies addressing nanoparticle (NP) interference with conventional toxicity assay systems, it appears that researchers still rely heavily on these assays, particularly for high-throughput screening (HTS) applications in order to generate “big” data for predictive toxicity approaches. Moreover, researchers often overlook investigating the different types of interference mechanisms as the type is evidently dependent on the type of assay system implemented. The approaches implemented in the literature appear to be not adequate as it often addresses only one type of interference mechanism with the exclusion of others. For example, interference of NPs that have entered cells would require intracellular assessment of their interference with fluorescent dyes, which has so far been neglected. The present study investigated the mechanisms of interference of gold NPs and silver NPs in assay systems implemented in HTS including optical interference as well as adsorption or catalysis. The conventional assays selected cover all optical read-out systems, that is, absorbance (XTT toxicity assay), fluorescence (CytoTox-ONE Homogeneous membrane integrity assay), and luminescence (CellTiter Glo luminescent assay). Furthermore, this study demonstrated NP quenching of fluorescent dyes also used in HTS (2′,7′-dichlorofluorescein, propidium iodide, and 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethyl-benzamidazolocarbocyanin iodide). To conclude, NP interference is, as such, not a novel concept, however, ignoring this aspect in HTS may jeopardize attempts in predictive toxicology. It should be mandatory to report the assessment of all mechanisms of interference within HTS, as well as to confirm results with label-free methodologies to ensure reliable big data generation for predictive toxicology.

scholarly journals Exploring Single-Cell Data with Deep Multitasking Neural Networks

2017 ◽  
Author(s):  
Matthew Amodio ◽  
David van Dijk ◽  
Krishnan Srinivasan ◽  
William S Chen ◽  
Hussein Mohsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Analysis ◽  
Experimental Design ◽  
Single Cell ◽  
Large Scale ◽  
Dengue Infection ◽  
Data Representation ◽  
Data Generation ◽  
Cell Data

AbstractBiomedical researchers are generating high-throughput, high-dimensional single-cell data at a staggering rate. As costs of data generation decrease, experimental design is moving towards measurement of many different single-cell samples in the same dataset. These samples can correspond to different patients, conditions, or treatments. While scalability of methods to datasets of these sizes is a challenge on its own, dealing with large-scale experimental design presents a whole new set of problems, including batch effects and sample comparison issues. Currently, there are no computational tools that can both handle large amounts of data in a scalable manner (many cells) and at the same time deal with many samples (many patients or conditions). Moreover, data analysis currently involves the use of different tools that each operate on their own data representation, not guaranteeing a synchronized analysis pipeline. For instance, data visualization methods can be disjoint and mismatched with the clustering method. For this purpose, we present SAUCIE, a deep neural network that leverages the high degree of parallelization and scalability offered by neural networks, as well as the deep representation of data that can be learned by them to perform many single-cell data analysis tasks, all on a unified representation.A well-known limitation of neural networks is their interpretability. Our key contribution here are newly formulated regularizations (penalties) that render features learned in hidden layers of the neural network interpretable. When large multi-patient datasets are fed into SAUCIE, the various hidden layers contain denoised and batch-corrected data, a low dimensional visualization, unsupervised clustering, as well as other information that can be used to explore the data. We show this capability by analyzing a newly generated 180-sample dataset consisting of T cells from dengue patients in India, measured with mass cytometry. We show that SAUCIE, for the first time, can batch correct and process this 11-million cell data to identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue on the basis of single-cell measurements.

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