Parallel and Cloud-Based Bioinformatics and Biomedicine

A special issue of High-Throughput (ISSN 2571-5135).

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 10503

Special Issue Editor


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Guest Editor
Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
Interests: bioinformatics, parallel computing, artificial intelligence, data mining

Special Issue Information

Dear Colleagues,

The availability of high-throughput platforms (e.g. next generation sequencing, microarray and mass spectrometry) and clinical diagnostic tools (e.g. medical imaging) is producing an overwhelming volume of experimental and clinical data. Thus, considering the complex analysis pipeline of the biomedical research, the bottleneck is more and more moving toward the storage, integration, and analysis of experimental data, as well as their correlation and integration with publicly available data banks. High Performance Computing offers the computational power and the storage to face this overwhelming availability of data, while Cloud Computing hides the complexity of computing infrastructures reducing the cost of the data analysis, and most importantly is changing the overall model of biomedical research and health provision.

Novel parallel architectures (e.g. CELL processors, GPUs, FPGA, hybrid CPU/FPGA) coupled with emerging programming models may overcome the limits posed by conventional computers to the mining and exploration of large amounts of data. Cloud Computing is able to offer scalable costs and increased reachability, availability and easiness of application use, and also the possibility to enforce collaboration among scientists. However, many problems remain to be solved, such as availability and safety of the data, privacy-related issues, availability of software platforms for rapid deployment, execution and billing of biomedical applications.

This Special Issue invites submissions on efficient algorithms, software tools and comprehensive data analysis pipelines for the preprocessing, integration and mining of molecular and clinical data; as well as submissions on applications of high performance computing and cloud computing in biology, medicine and clinical practice.

Prof. Dr. Mario Cannataro
Guest Editor

Manuscript Submission Information

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Keywords

  • Parallel bioinformatics algorithms
  • Parallel preprocessing of omics and clinical data
  • Parallel visualization and exploration of omics and clinical data
  • High-throughput comprehensive bioinformatics pipelines
  • Computing environments for large scale collaboration
  • Scientific workflows in bioinformatics and biomedicine
  • Parallel processing of bio-signals and bio-images
  • Cloud computing for bioinformatics and biomedicine
  • Cloud computing for health systems
  • Privacy issues for cloud-based biomedical applications
  • Services for bioinformatics and biomedicine
  • Large scale biological and biomedical databases
  • Integration and analysis of molecular and clinical data
  • Ontologies in biology and medicine
  • P4 (predictive, preventive, personalized and participatory) medicine

Published Papers (2 papers)

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Research

14 pages, 1439 KiB  
Article
Computational Convolution of SELDI Data for the Diagnosis of Alzheimer’s Disease
by Destiny E. O. Anyaiwe, Gautam B. Singh, George D. Wilson and Timothy J. Geddes
High-Throughput 2018, 7(2), 14; https://doi.org/10.3390/ht7020014 - 17 May 2018
Cited by 1 | Viewed by 3409
Abstract
Alzheimer’s disease is rapidly becoming an endemic for people over the age of 65. A vital path towards reversing this ominous trend is the building of reliable diagnostic devices for definite and early diagnoses in lieu of the longitudinal, usually inconclusive and non-generalize-able [...] Read more.
Alzheimer’s disease is rapidly becoming an endemic for people over the age of 65. A vital path towards reversing this ominous trend is the building of reliable diagnostic devices for definite and early diagnoses in lieu of the longitudinal, usually inconclusive and non-generalize-able methods currently in use. In this article, we present a survey of methods for mining pools of mass spectrometer saliva data in relation to diagnosing Alzheimer’s disease. The computational methods provides new approaches for appropriately gleaning latent information from mass spectra data. They improve traditional machine learning algorithms and are most fit for handling matrix data points including solving problems beyond protein identifications and biomarker discovery. Full article
(This article belongs to the Special Issue Parallel and Cloud-Based Bioinformatics and Biomedicine)
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16 pages, 1594 KiB  
Article
Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data—fMRI Study
by Taban Eslami and Fahad Saeed
High-Throughput 2018, 7(2), 11; https://doi.org/10.3390/ht7020011 - 20 Apr 2018
Cited by 17 | Viewed by 6386
Abstract
Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is [...] Read more.
Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is widely used for constructing functional network and studying dynamic functional connectivity of the brain. These are useful measures for understanding the effects of brain disorders on connectivities among brain regions. The fMRI scanners produce huge number of voxels and using traditional central processing unit (CPU)-based techniques for computing pairwise correlations is very time consuming especially when large number of subjects are being studied. In this paper, we propose a graphics processing unit (GPU)-based algorithm called Fast-GPU-PCC for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns N ( N 1 ) / 2 correlation coefficients located at strictly upper triangle part of the correlation matrix. Storing correlations in a one-dimensional array with the order as proposed in this paper is useful for further usage. Our experiments on real and synthetic fMRI data for different number of voxels and varying length of time series show that the proposed approach outperformed state of the art GPU-based techniques as well as the sequential CPU-based versions. We show that Fast-GPU-PCC runs 62 times faster than CPU-based version and about 2 to 3 times faster than two other state of the art GPU-based methods. Full article
(This article belongs to the Special Issue Parallel and Cloud-Based Bioinformatics and Biomedicine)
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