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.

ScaDS Dresden/Leipzig – A competence center for collaborative big data research

2018 ◽  
Vol 60 (5-6) ◽  
pp. 327-333 ◽  
Author(s):  
René Jäkel ◽  
Eric Peukert ◽  
Wolfgang E. Nagel ◽  
Erhard Rahm
Keyword(s):  
Big Data ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Data Intensive ◽  
Innovative Methods ◽  
Huge Data ◽  
Wide Range ◽  
Resource Requirements ◽  
Visualization Of Data ◽  
Data Intensive Applications

Abstract The efficient and intelligent handling of large, often distributed and heterogeneous data sets increasingly determines the scientific and economic competitiveness in most application areas. Mobile applications, social networks, multimedia collections, sensor networks, data intense scientific experiments, and complex simulations nowadays generate a huge data deluge. Nonetheless, processing and analyzing these data sets with innovative methods open up new opportunities for its exploitation and new insights. Nevertheless, the resulting resource requirements exceed usually the possibilities of state-of-the-art methods for the acquisition, integration, analysis and visualization of data and are summarized under the term big data. ScaDS Dresden/Leipzig, as one Germany-wide competence center for collaborative big data research, bundles efforts to realize data-intensive applications for a wide range of applications in science and industry. In this article, we present the basic concept of the competence center and give insights in some of its research topics.

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.

scholarly journals Analytical Design of the Dis Architecture: The Hybrid Model

2020 ◽  
Vol 9 (5) ◽  
pp. 1032-1036
Keyword(s):  
Architectural Design ◽  
Fault Tolerant ◽  
Current Model ◽  
Data Sets ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Sustainable Technologies ◽  
Huge Data ◽  
Design Changes ◽  
Physical Infrastructure

In the last decades, and due to emergence of Internet appliance, there is a strategical increase in the usage of data which had a high impact on the storage and mining technologies. It is also observed that the scientific/research field’s produces the zig-zag structure of data viz., structured, semi-structured, and unstructured data. Comparably, processing of such data is relatively increased due to rugged requirements. There are sustainable technologies to address the challenges and to expedite scalable services via effective physical infrastructure (in terms of mining), smart networking solutions, and useful software approaches. Indeed, the Cloud computing aims at data-intensive computing, by facilitating scalable processing of huge data. But still, the problem remains unaddressed with reference to huge data and conversely the data is growing exponentially faster. At this juncture, the recommendable algorithm is, the well-known model i.e., MapReduce, to compress the huge and voluminous data. Conceptualization of any problem with the current model is, less fault-tolerant and reliability, which may be surmounted by Hadoop architecture. On Contrary case, Hadoop is fault tolerant, and has the high throughput which is recommendable for applications having huge volume of data sets, file system requiring the streaming access. The paper examines and unravels, what efficient architectural/design changes are necessary to bring the benefits of the Everest model, HBase algorithm, and the existing MR algorithms.

Pathway Biology Approach to Medicine

2009 ◽  
pp. 1-10
Author(s):  
Peter Ghazal
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Single Gene ◽  
System Level ◽  
Experimental Medicine ◽  
Data Sets ◽  
Biological Processes ◽  
Complex Information ◽  
New Biomarkers ◽  
High Throughput Experiments

An increasing number of biological experiments and more recently clinical based studies are being conducted using large-scale genomic, proteomic and metabolomic techniques which generate high-dimensional data sets. Such approaches require the adoption of both hypothesis and data driven strategies in the analysis and interpretation of results. In particular, data-mining and pattern recognition methodologies have proven particularly useful in this field. The increasing amount of information available from high-throughput experiments has initiated a move from focussed, single gene and protein investigations abstract Systems biology provides a new approach to studying, analyzing, and ultimately controlling biological processes. Biological pathways represent a key sub-system level of organization that seamlessly perform complex information processing and control tasks. The aim of pathway biology is to map and understand the cause-effect relationships and dependencies associated with the complex interactions of biological networks and systems. Drugs that therapeutically modulate the biological processes of disease are often developed with limited knowledge of the underlying complexity of their specific targets. Considering the combinatorial complexity from the outset might help identify potential causal relationships that could lead to a better understanding of the drug-target biology as well as provide new biomarkers for modelling diagnosis and treatment response in patients. This chapter discusses the use of a pathway biology approach to modelling biological processes and providing a new framework for experimental medicine in the post-genomic era.

scholarly journals A Hybrid Scheduler for Many Task Computing in Big Data Systems

2017 ◽  
Vol 27 (2) ◽  
pp. 385-399 ◽  
Author(s):  
Laura Vasiliu ◽  
Florin Pop ◽  
Catalin Negru ◽  
Mariana Mocanu ◽  
Valentin Cristea ◽  
...  
Keyword(s):  
Big Data ◽  
Scheduling Algorithm ◽  
Rapid Evolution ◽  
Data Sets ◽  
Data Systems ◽  
Data Intensive ◽  
Performance Requirements ◽  
Huge Data ◽  
Hybrid Solution ◽  
Big Data Systems

AbstractWith the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

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