scholarly journals Computational Strategies for Scalable Genomics Analysis

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1017
Author(s):  
Lizhen Shi ◽  
Zhong Wang
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Scale Up ◽  
Next Generation Dna Sequencing ◽  
Significant Challenge ◽  
Sequencing Technologies ◽  
Software Algorithms ◽  
Big Data Technologies ◽  
Pros And Cons ◽  
Computing Infrastructure

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

scholarly journals Melanoma expression analysis with Big Data technologies

2017 ◽  
Author(s):  
Alicia Fernandez-Rovira ◽  
Rocio Lavado-Valenzuela ◽  
Miguel Ángel Berciano Guerrero ◽  
Ismael Navas-Delgado ◽  
José F Aldana-Montes
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Immune System ◽  
Clinical Study ◽  
Tumor Regression ◽  
Scale Up ◽  
Clinical Information ◽  
Initial Study ◽  
Expression Platform ◽  
Big Data Technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.

scholarly journals Melanoma expression analysis with Big Data technologies

2017 ◽  
Author(s):  
Alicia Fernandez-Rovira ◽  
Rocio Lavado ◽  
Miguel Ángel Berciano Guerrero ◽  
Ismael Navas-Delgado ◽  
José F Aldana-Montes
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Immune System ◽  
Clinical Study ◽  
Tumor Regression ◽  
Scale Up ◽  
Clinical Information ◽  
Initial Study ◽  
Expression Platform ◽  
Big Data Technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.

scholarly journals An Auto-Scaling Framework for Analyzing Big Data in the Cloud Environment

2019 ◽  
Vol 9 (7) ◽  
pp. 1417 ◽  
Author(s):  
Rachana Jannapureddy ◽  
Quoc-Tuan Vien ◽  
Purav Shah ◽  
Ramona Trestian
Keyword(s):  
Big Data ◽  
Real Time ◽  
Resource Use ◽  
Scale Up ◽  
Cost Effective ◽  
Dynamic Scaling ◽  
Cloud Environment ◽  
Time Data ◽  
Auto Scaling ◽  
Computing Infrastructure

Processing big data on traditional computing infrastructure is a challenge as the volume of data is large and thus high computational complexity. Recently, Apache Hadoop has emerged as a distributed computing infrastructure to deal with big data. Adopting Hadoop to dynamically adjust its computing resources based on real-time workload is itself a demanding task, thus conventionally a pre-configuration with adequate resources to compute the peak data load is set up. However, this may cause a considerable wastage of computing resources when the usage levels are much lower than the preset load. In consideration of this, this paper investigates an auto-scaling framework on cloud environment aiming to minimise the cost of resource use by automatically adjusting the virtual nodes depending on the real-time data load. A cost-effective auto-scaling (CEAS) framework is first proposed for an Amazon Web Services (AWS) Cloud environment. The proposed CEAS framework allows us to scale the computing resources of Hadoop cluster so as to either reduce the computing resource use when the workload is low or scale-up the computing resources to speed up the data processing and analysis within an adequate time. To validate the effectiveness of the proposed framework, a case study with real-time sentiment analysis on the universities’ tweets is provided to analyse the reviews/tweets of the people posted on social media. Such a dynamic scaling method offers a reference to improving the Twitter data analysis in a more cost-effective and flexible way.

scholarly journals Melanoma expression analysis with Big Data technologies

2017 ◽  
Author(s):  
Alicia Fernandez-Rovira ◽  
Rocio Lavado-Valenzuela ◽  
Miguel Ángel Berciano Guerrero ◽  
Ismael Navas-Delgado ◽  
José F Aldana-Montes
Keyword(s):  
Gene Expression ◽  
Big Data ◽  
Immune System ◽  
Clinical Study ◽  
Tumor Regression ◽  
Scale Up ◽  
Clinical Information ◽  
Initial Study ◽  
Expression Platform ◽  
Big Data Technologies

Melanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.

Teaching Medical Informatics: Teaching on the Seams of Disciplines, Cultures, Traditions

1989 ◽  
Vol 28 (04) ◽  
pp. 273-280 ◽  
Author(s):  
J. Möhr
Keyword(s):  
Computer Science ◽  
Medical Informatics ◽  
Pros And Cons

Abstract:This paper reviews different concepts of medical informatics and identifies two families of approaches to education in it: a “specialist” approach, whereby medical informatics is taught as a specialization track for established disciplines like medicine, computer science, nursing, engineering, etc., and a “generalistic” approach, whereby it is taught as an integrated discipline incorporating essential traits of the aforementioned disciplines. The pros and cons of these approaches are outlined. The need to accommodate specific requirements of education is emphasized and these are identified, together with an outline of particular challenges that we are facing.

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