Contents, vehicles, and complex data analysis in neuroscience

With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

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Unknown input decoupling and estimation in observer design for Boolean control networks * *This work is supported by the Federal State of Rhineland- Palatinate, Germany in the framework of the project \Complex Data Analysis in Life Sciences and Biotechnology (BioComp)".

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2017.08.649 ◽

2017 ◽

Vol 50 (1) ◽

pp. 2917-2922 ◽

Cited By ~ 2

Author(s):

Zhihua Zhang ◽

Thomas Leifeld ◽

Ping Zhang

Keyword(s):

Data Analysis ◽

Life Sciences ◽

Observer Design ◽

Federal State ◽

Unknown Input ◽

Complex Data ◽

Control Networks

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Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

10.1101/116962 ◽

2017 ◽

Cited By ~ 3

Author(s):

Baekdoo Kim ◽

Thahmina Ali ◽

Carlos Lijeron ◽

Enis Afgan ◽

Konstantinos Krampis

Keyword(s):

Data Analysis ◽

Cloud Service ◽

Application Programming Interface ◽

Single Step ◽

Easy Access ◽

Complex Data ◽

The Galaxy ◽

Ngs Data Analysis ◽

Single Data ◽

Ngs Data

ABSTRACTBackgroundProcessing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform.FindingsWe present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines is as simple as running a single bioinformatics tool. This is achieved through a “meta-script” that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipelne output is post-processed using the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser.ConclusionsThe goal of our approach is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider,. Besides end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

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Kernel Generative Topographic Mapping of Protein Sequences

Medical Applications of Intelligent Data Analysis - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-4666-1803-9.ch013 ◽

2012 ◽

pp. 195-208

Author(s):

M.I. Cardenas ◽

A. Vellido ◽

I. Olier ◽

X. Rovira ◽

J. Giraldo

Keyword(s):

Data Analysis ◽

Kernel Method ◽

Protein Sequences ◽

Topographic Mapping ◽

Complex Data ◽

Generative Topographic Mapping ◽

Protein Amino Acid ◽

Genomics And Proteomics ◽

The World ◽

Symbolic Sequences

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligence data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-couple receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.

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Anomaly Detection for Inferring Social Structure

Social Computing ◽

10.4018/978-1-60566-984-7.ch118 ◽

2010 ◽

pp. 1797-1803

Author(s):

Lisa Friedland

Keyword(s):

Data Analysis ◽

Anomaly Detection ◽

Social Structure ◽

Small Groups ◽

Analysis Data ◽

Data Sets ◽

Complex Data ◽

Detection Approach ◽

Complex Data Sets ◽

Data Points

In traditional data analysis, data points lie in a Cartesian space, and an analyst asks certain questions: (1) What distribution can I fit to the data? (2) Which points are outliers? (3) Are there distinct clusters or substructure? Today, data mining treats richer and richer types of data. Social networks encode information about people and their communities; relational data sets incorporate multiple types of entities and links; and temporal information describes the dynamics of these systems. With such semantically complex data sets, a greater variety of patterns can be described and views constructed of the data. This article describes a specific social structure that may be present in such data sources and presents a framework for detecting it. The goal is to identify tribes, or small groups of individuals that intentionally coordinate their behavior—individuals with enough in common that they are unlikely to be acting independently. While this task can only be conceived of in a domain of interacting entities, the solution techniques return to the traditional data analysis questions. In order to find hidden structure (3), we use an anomaly detection approach: develop a model to describe the data (1), then identify outliers (2).

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Symbolic Data Analysis

International Journal of Signs and Semiotic Systems ◽

10.4018/ijsss.2014010101 ◽

2014 ◽

Vol 3 (1) ◽

pp. 1-9

Author(s):

Sandra Elizabeth González Císaro ◽

Héctor Oscar Nigro

Keyword(s):

Data Analysis ◽

Complex Data ◽

New Paradigm ◽

Symbolic Data Analysis ◽

Standard Data ◽

Symbolic Data ◽

The World ◽

New Type ◽

Mining Complex ◽

Internal Variation

Standard data mining techniques no longer adequately represent the complexity of the world. So, a new paradigm is necessary. Symbolic Data Analysis is a new type of data analysis that allows us to represent the complexity of reality, maintaining the internal variation and structure developed by Diday (2003). This new paradigm is based on the concept of symbolic object, which is a mathematical model of a concept. In this article the authors are going to present the fundamentals of the symbolic data analysis paradigm and the symbolic object concept. Theoretical aspects and examples allow the authors to understand the SDA paradigm as a tool for mining complex data.

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