scholarly journals Systems Biology in the Context of Big Data and Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Md. Altaf-Ul-Amin ◽  
Farit Mochamad Afendi ◽  
Samuel Kuria Kiboi ◽  
Shigehiko Kanaya
Keyword(s):  
Big Data ◽  
Systems Biology ◽  
Biological Networks ◽  
Network Biology ◽  
Biological Data ◽  
The Other ◽  
System Level ◽  
Data Handling ◽  
Data Intensive ◽  
Cellular Components

Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.

Coding Inequality

2020 ◽  
pp. 100-117
Author(s):  
Sarah Brayne
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Social Inequalities ◽  
Big Data Analytics ◽  
The Other ◽  
Surveillance Network ◽  
Data Intensive ◽  
The One ◽  
Reproduction Of Inequality

This chapter looks at the promise and peril of police use of big data analytics for inequality. On the one hand, big data analytics may be a means by which to ameliorate persistent inequalities in policing. Data can be used to “police the police” and replace unparticularized suspicion of racial minorities and human exaggeration of patterns with less biased predictions of risk. On the other hand, data-intensive police surveillance practices are implicated in the reproduction of inequality in at least four ways: by deepening the surveillance of individuals already under suspicion, codifying a secondary surveillance network of individuals with no direct police contact, widening the criminal justice dragnet unequally, and leading people to avoid institutions that collect data and are fundamental to social integration. Crucially, as currently implemented, “data-driven” decision-making techwashes, both obscuring and amplifying social inequalities under a patina of objectivity.

scholarly journals NetConfer: a web application for comparative analysis of multiple biological networks

BMC Biology ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Sunil Nagpal ◽  
Krishanu Das Baksi ◽  
Bhusan K. Kuntal ◽  
Sharmila S. Mande
Keyword(s):  
Comparative Analysis ◽  
Biological Networks ◽  
Web Application ◽  
Biological Systems ◽  
Batch Process ◽  
Biological Data ◽  
Data Intensive ◽  
Multiple Networks ◽  
Biological Data Analysis ◽  
Multiple Network

Abstract Background Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks. Comparisons of multiple networks can help in identifying variations across different biological systems, thereby providing additional insights. However, while a number of online and stand-alone tools exist for generating, analyzing, and visualizing individual biological networks, the utility to batch process and comprehensively compare multiple networks is limited. Results Here, we present a graphical user interface (GUI)-based web application which implements multiple network comparison methodologies and presents them in the form of organized analysis workflows. Dedicated comparative visualization modules are provided to the end-users for obtaining easy to comprehend, insightful, and meaningful comparisons of various biological networks. We demonstrate the utility and power of our tool using publicly available microbial and gene expression data. Conclusion NetConfer tool is developed keeping in mind the requirements of researchers working in the field of biological data analysis with limited programming expertise. It is also expected to be useful for advanced users from biological as well as other domains (working with association networks), benefiting from provided ready-made workflows, as they allow to focus directly on the results without worrying about the implementation. While the web version allows using this application without installation and dependency requirements, a stand-alone version has also been supplemented to accommodate the offline requirement of processing large networks.

scholarly journals A Glimpse to Background and Characteristics of Major Molecular Biological Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Md. Altaf-Ul-Amin ◽  
Tetsuo Katsuragi ◽  
Tetsuo Sato ◽  
Shigehiko Kanaya
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
System Level ◽  
Data Sets ◽  
Topological Properties ◽  
Comprehensive Understanding ◽  
Data Intensive ◽  
Huge Data ◽  
Mutual Relations ◽  
Level Analysis

Recently, biology has become a data intensive science because of huge data sets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. These huge datasets have paved the way for system-level analysis of the processes and subprocesses of the cell. For system-level understanding, initially the elements of a system are connected based on their mutual relations and a network is formed. Among omics researchers, construction and analysis of biological networks have become highly popular. In this review, we briefly discuss both the biological background and topological properties of major types of omics networks to facilitate a comprehensive understanding and to conceptualize the foundation of network biology.

scholarly journals Coming Together of Big Data and Cloud Computing : A Review

2020 ◽  
pp. 118-137
Author(s):  
Muneeba Afzal Mukhdoomi ◽  
Ashish Oberoi ◽  
Ankur Gupta
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Fault Tolerant ◽  
The Other ◽  
Data Sets ◽  
Application Software ◽  
Data Handling ◽  
Computing Power ◽  
Rapid Pace ◽  
Tools And Techniques

Big data stands for sheer amount of data that is growing unceasingly at a rapid pace. Big Data demands high-powered, robust, reliable, fault-tolerant tools and techniques in order to make it convenient to process, analyse and uproot new insights from Big Data. Big data refers to huge, heterogeneous amount of details, facts and data generating at constantly rising rate. The data sets in Big Data are too bulky or extensive, as a result classical data handling application software are not competent enough to administer them. On the other hand, Cloud computing is a resourceful technology providing high computing power, scalability, computing resources as and when required for processing, storage, analytics and visualization of Big Data. Therefore, cloud computing can be regarded as a feasible and applicable technology which promises to handle Big Data challenges and also provides here and now infrastructures with all the mandatory resources. This paper will mainly review processing of big data cloud using Hadoop and spark in cloud, advantages of driving Big Data using cloud computing and applications of Big data in Cloud.

scholarly journals An Application of Big Data in Social Media Anomaly Detection using Weight Based Technique to Compare Performance of PIG and HIVE

2019 ◽  
Vol 8 (10) ◽  
pp. 2492-2497
Keyword(s):  
Social Media ◽  
Big Data ◽  
Experimental Analysis ◽  
The Other ◽  
Sensitive Information ◽  
Data Handling ◽  
Twitter Data ◽  
Huge Data ◽  
The Social ◽  
A New Technique

Social media is become one of the most popular application. Commonly social media is used for communication and social activities. Thus a significant amount of data is produced in these platforms and handling of these data requires advance data handling techniques thus big data is used to deal with such huge data. On the other hand, now in these days attackers and phishers are also active on social media. These attackers create fake profiles and trap the users to still their confidential and sensitive information. In this context the fake profiles are one of the serious problems in these days in social media. In this presented work a new technique for detecting the social media anomaly profile is prepared and their implementation is described in this paper. In addition of that the experimental analysis on real twitter profiles are also performed for 1200 profile features. To process these data two BIG data utilities are used namely PIG and HIVE is used. These profile features are collected from the live twitter data and evaluation of different profiles. The experimental results are compared for both the utilities (i.e. PIG and Hive) to demonstrate the successfully identification of legitimate and anomaly profiles.

scholarly journals DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

AbstractData variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

scholarly journals Autonomy for Space Robots: Past, Present, and Future

2021 ◽  
Author(s):  
Issa A.D. Nesnas ◽  
Lorraine M. Fesq ◽  
Richard A. Volpe
Keyword(s):  
Space Exploration ◽  
System Level ◽  
Physical Interaction ◽  
Data Handling ◽  
Proximity Operations ◽  
The Past ◽  
Surface Mobility ◽  
Spacecraft Navigation ◽  
New Frontier ◽  
Near Earth Objects

Abstract Purpose of Review The purpose of this review is to highlight space autonomy advances across mission phases, capture the anticipated need for autonomy and associated rationale, assess state of the practice, and share thoughts for future advancements that could lead to a new frontier in space exploration. Recent Findings Over the past two decades, several autonomous functions and system-level capabilities have been demonstrated and used in spacecraft operations. In spite of that, spacecraft today remain largely reliant on ground in the loop to assess situations and plan next actions, using pre-scripted command sequences. Advances have been made across mission phases including spacecraft navigation; proximity operations; entry, descent, and landing; surface mobility and manipulation; and data handling. But past successful practices may not be sustainable for future exploration. The ability of ground operators to predict the outcome of their plans seriously diminishes when platforms physically interact with planetary bodies, as has been experienced in two decades of Mars surface operations. This results from uncertainties that arise due to limited knowledge, complex physical interaction with the environment, and limitations of associated models. Summary Robotics and autonomy are synergistic, wherein robotics provides flexibility, autonomy exercises it to more effectively and robustly explore unknown worlds. Such capabilities can be substantially advanced by leveraging the rapid growth in SmallSats, the relative accessibility of near-Earth objects, and the recent increase in launch opportunities.

Big data, integrative omics and network biology

2021 ◽  
Author(s):  
Priya Tolani ◽  
Srishti Gupta ◽  
Kirti Yadav ◽  
Suruchi Aggarwal ◽  
Amit Kumar Yadav
Keyword(s):  
Big Data ◽  
Network Biology ◽  
Integrative Omics

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

Social networks to biological networks: systems biology of Mycobacterium tuberculosis

2013 ◽  
Vol 9 (7) ◽  
pp. 1584 ◽  
Author(s):  
Rohit Vashisht ◽  
Anshu Bhardwaj ◽  
OSDD Consortium ◽  
Samir K. Brahmachari
Keyword(s):  
Social Networks ◽  
Mycobacterium Tuberculosis ◽  
Systems Biology ◽  
Biological Networks
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