scholarly journals Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models

2020 ◽  
Vol 21 (11) ◽  
pp. 3754 ◽  
Author(s):  
Erdem Bangi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Cost Effective ◽  
Experimental Models ◽  
Cancer Data ◽  
Functional Studies ◽  
Cancer Models ◽  
Effective Manner

Rapid development of high throughput genome analysis technologies accompanied by significant reduction in costs has led to the accumulation of an incredible amount of data during the last decade. The emergence of big data has had a particularly significant impact in biomedical research by providing unprecedented, systems-level access to many disease states including cancer, and has created promising opportunities as well as new challenges. Arguably, the most significant challenge cancer research currently faces is finding effective ways to use big data to improve our understanding of molecular mechanisms underlying tumorigenesis and developing effective new therapies. Functional exploration of these datasets and testing predictions from computational approaches using experimental models to interrogate their biological relevance is a key step towards achieving this goal. Given the daunting scale and complexity of the big data available, experimental systems like Drosophila that allow large-scale functional studies and complex genetic manipulations in a rapid, cost-effective manner will be of particular importance for this purpose. Findings from these large-scale exploratory functional studies can then be used to formulate more specific hypotheses to be explored in mammalian models. Here, I will discuss several strategies for functional exploration of big cancer data using Drosophila cancer models.

scholarly journals A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs

Molecules ◽  
2019 ◽  
Vol 24 (11) ◽  
pp. 2097 ◽  
Author(s):  
Ambrose Plante ◽  
Derek M. Shore ◽  
Giulia Morra ◽  
George Khelashvili ◽  
Harel Weinstein
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Md Simulations ◽  
Machine Learning Approach ◽  
And Function ◽  
Functional Mechanisms ◽  
G Protein Coupled ◽  
Large Scale Simulations

G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.

Enterprise Crowdsourcing Platform to Improve HR Workflow

2020 ◽  
Vol 9 (5) ◽  
pp. 5-8 ◽  
Author(s):  
G. Zuev
Keyword(s):  
Large Scale ◽  
Best Practice ◽  
Cost Effective ◽  
Hr Management ◽  
Practical Project ◽  
Effective Manner ◽  
Wide Range ◽  
Hr Managers ◽  
Short Time ◽  
Small Parts

Crowdsourcing technologies may solve a wide range of business issues: improve efficiency of HR management, increase customer loyalty and maximize economic efficiency of whole enterprise. The recent years best practice has shown how crowdsourcing is gaining particular relevance of human resource management, allowing HR managers to resolve organization relevant problems in quick and cost-effective manner. Important advantage of crowdsourcing сomes from his main ability: decomposition of tasks into small parts and the ability to perform it’s remotely, via Internet. Thanks to this, not only large corporations, but also small and medium-sized businesses can execute a large-scale projects in a short time. This article discusses the main approaches and principles of practical project management via crowdsourcing platforms, using as the example “Beorg Smart Vision” solution.

scholarly journals Military Simulation Big Data: Background, State of the Art, and Challenges

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Xiao Song ◽  
Yulin Wu ◽  
Yaofei Ma ◽  
Yong Cui ◽  
Guanghong Gong
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Rapid Development ◽  
Military Training ◽  
Great Success ◽  
Service Oriented Architectures ◽  
Weapon Systems ◽  
Service Oriented ◽  
High Resolution Models ◽  
Future Work

Big data technology has undergone rapid development and attained great success in the business field. Military simulation (MS) is another application domain producing massive datasets created by high-resolution models and large-scale simulations. It is used to study complicated problems such as weapon systems acquisition, combat analysis, and military training. This paper firstly reviewed several large-scale military simulations producing big data (MS big data) for a variety of usages and summarized the main characteristics of result data. Then we looked at the technical details involving the generation, collection, processing, and analysis of MS big data. Two frameworks were also surveyed to trace the development of the underlying software platform. Finally, we identified some key challenges and proposed a framework as a basis for future work. This framework considered both the simulation and big data management at the same time based on layered and service oriented architectures. The objective of this review is to help interested researchers learn the key points of MS big data and provide references for tackling the big data problem and performing further research.

scholarly journals C. elegans PVD Neurons: A Platform for Functionally Validating and Characterizing Neuropsychiatric Risk Genes

2016 ◽  
Author(s):  
Cristina Aguirre-Chen ◽  
Nuri Kim ◽  
Olivia Mendivil Ramos ◽  
Melissa Kramer ◽  
W. Richard McCombie ◽  
...  
Keyword(s):  
Protein Function ◽  
Molecular Mechanisms ◽  
Neuronal Development ◽  
De Novo ◽  
Genetic Interaction ◽  
Cost Effective ◽  
Chromatin Remodelers ◽  
C Elegans ◽  
Effective Manner

AbstractOne of the primary challenges in the field of psychiatric genetics is the lack of an in vivo model system in which to functionally validate candidate neuropsychiatric risk genes (NRGs) in a rapid and cost-effective manner1−3. To overcome this obstacle, we performed a candidate-based RNAi screen in which C. elegans orthologs of human NRGs were assayed for dendritic arborization and cell specification defects using C. elegans PVD neurons. Of 66 NRGs, identified via exome sequencing of autism (ASD)4 or schizophrenia (SCZ)5−9 probands and whose mutations are de novo and predicted to result in a complete or partial loss of protein function, the C. elegans orthologs of 7 NRGs were found to be required for proper neuronal development and represent a variety of functional classes, including transcriptional regulators and chromatin remodelers, molecular chaperones, and cytoskeleton-related proteins. Notably, the positive hit rate, when selectively assaying C. elegans orthologs of ASD and SCZ NRGs, is enriched >14-fold as compared to unbiased RNAi screening10. Furthermore, we find that RNAi phenotypes associated with the depletion of NRG orthologs is recapitulated in genetic mutant animals, and, via genetic interaction studies, we show that the NRG ortholog of ANK2, unc-44, is required for SAX-7/MNR-1/DMA-1 signaling. Collectively, our studies demonstrate that C. elegans PVD neurons are a tractable model in which to discover and dissect the fundamental molecular mechanisms underlying neuropsychiatric disease pathogenesis.

scholarly journals Novel holistic architecture for analytical operation on sensory data relayed as cloud services

2020 ◽  
Vol 10 (4) ◽  
pp. 4322
Author(s):  
Manujakshi B. C ◽  
K. B. Ramesh
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Cloud Services ◽  
Sensor Nodes ◽  
Sensory Data ◽  
Energy Constrained ◽  
Analytical Operation

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

scholarly journals Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

2021 ◽  
Vol 12 ◽  
Author(s):  
He Miao ◽  
Song Chen ◽  
Renyu Ding
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Rapid Development ◽  
Cecal Ligation ◽  
Research Area ◽  
Multiple Organ ◽  
Protein Characterization ◽  
Host Responses ◽  
Related Data ◽  
Proteome Changes

Sepsis is a complex syndrome promoted by pathogenic and host factors; it is characterized by dysregulated host responses and multiple organ dysfunction, which can lead to death. However, its underlying molecular mechanisms remain unknown. Proteomics, as a biotechnology research area in the post-genomic era, paves the way for large-scale protein characterization. With the rapid development of proteomics technology, various approaches can be used to monitor proteome changes and identify differentially expressed proteins in sepsis, which may help to understand the pathophysiological process of sepsis. Although previous reports have summarized proteomics-related data on the diagnosis of sepsis and sepsis-related biomarkers, the present review aims to comprehensively summarize the available literature concerning “sepsis”, “proteomics”, “cecal ligation and puncture”, “lipopolysaccharide”, and “post-translational modifications” in relation to proteomics research to provide novel insights into the molecular mechanisms of sepsis.

scholarly journals Evaluation of big data frameworks for analysis of smart grids

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Hasan Ansari ◽  
Vahid Tabatab Vakili ◽  
Behnam Bahrak
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Cyber Security ◽  
Smart Grids ◽  
Large Scale ◽  
Rapid Development ◽  
Massive Data ◽  
Grid Data ◽  
Data Generator ◽  
And Performance

AbstractWith the rapid development of smart grids and increasing data collected in these networks, analyzing this massive data for applications such as marketing, cyber-security, and performance analysis, has gained popularity. This paper focuses on analysis and performance evaluation of big data frameworks that are proposed for handling smart grid data. Since obtaining large amounts of smart grid data is difficult due to privacy concerns, we propose and implement a large scale smart grid data generator to produce massive data under conditions similar to those in real smart grids. We use four open source big data frameworks namely Hadoop-Hbase, Cassandra, Elasticsearch, and MongoDB, in our implementation. Finally, we evaluate the performance of different frameworks on smart grid big data and present a performance benchmark that includes common data analysis techniques on smart grid data.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

Serum-based protein biomarkers for detection of lung cancer

2014 ◽  
Vol 9 (4) ◽  
pp. 341-358
Author(s):  
Shilpa Bhatnagar ◽  
Deepshikha Katare ◽  
Swatantra Jain
Keyword(s):  
Lung Cancer ◽  
Cancer Biology ◽  
Early Stage ◽  
Expression Patterns ◽  
Cost Effective ◽  
Diagnostic Tools ◽  
Protein Biomarkers ◽  
Functional Studies ◽  
Molecular Tests ◽  
Effective Manner

AbstractLung cancer is one of the most common cancers in terms of both incidence and mortality.The major reasons for the increasing number of deaths from lung cancer are late detection and lack of effective therapies. To improve our understanding of lung cancer biology, there is urgent need for blood-based, non-invasive molecular tests to assist in its detection in a cost-effective manner at an early stage when curative interventions are still possible. Recent advances in proteomic technology have provided extensive, high throughput analytical tools for identification, characterization and functional studies of proteomes. Changes in protein expression patterns in response to stimuli can serve as indicators or biomarkers of biological and pathological processes as well as physiological and pharmacological responses to drug treatment, thus aiding in early diagnosis and prognosis of disease. However, only a few biomarkers have been approved by the FDA to date for screening and diagnostic purposes. This review provides a brief overview of currently available proteomic techniques, their applications and limitations and the current state of knowledge about important serum biomarkers in lung cancer and their potential value as prognostic and diagnostic tools.

scholarly journals Hybrid Mobile-App. on Multi-MEC Platforms in NFV Environment

2018 ◽  
Vol 7 (4.38) ◽  
pp. 383
Author(s):  
Jinsul Kim ◽  
Akm Ashiquzzaman ◽  
Van Quan Nguyen ◽  
Sang Woo Kim
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Scale ◽  
Web Applications ◽  
Rapid Development ◽  
Query Languages ◽  
Vital Role ◽  
Mobile App ◽  
Network Function Virtualization ◽  
Nosql Databases

In recent times, practicality of web applications has become more reliant upon big-data orientated materials such 4K videos, hi-def. resolution images, lossless audios and massive texts. Structured Query Languages (SQL) faces compatibility issues with large scale databases. Because of this data storage problem, NoSQL databases are used for storing big-data. NoSQL databases have been recently gaining traction with many options such MongoDB, CouchDB, Redis and Apache Cassandra. One of the major restrictions companies, enterprises and developers encounter during developing an application is multiplicative cost of building a native programing across different platforms. Besides, network Function Virtualization (NFV) plays a vital role for providing services for utilizing such applications in larger and more effective scale. Hence, in this paper, we discussed our main motivation behind selecting Iconic Framework, a hybrid system for rapid development real-time application based on Firebase in the NFV environment cooperating with Mobile Edge Computing (MEC). As a result, this approach provides comparatively flexible features.  

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