Cloud-Scale Genomic Signals Processing for Robust Large-Scale Cancer Genomic Microarray Data Analysis

Abstract

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring forth oncological inference to the bioinformatics community through the analysis of large-scale cancer genomic (LSCG) DNA and mRNA microarray data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological interpretation by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale distributed parallel (CSDP) separable 1-D wavelet decomposition technique for denoising through differential expression thresholding and classification of LSCG microarray data. This research presents a novel methodology that utilizes a CSDP separable 1-D method for wavelet-based transformation in order to initialize a threshold which will retain significantly expressed genes through the denoising process for robust classification of cancer patients. Additionally, the overall study was implemented and encompassed within CSDP environment. The utilization of cloud computing and wavelet-based thresholding for denoising was used for the classification of samples within the Global Cancer Map, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas. The results proved that separable 1-D parallel distributed wavelet denoising in the cloud and differential expression thresholding increased the computational performance and enabled the generation of higher quality LSCG microarray datasets, which led to more accurate classification results.

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

“…• Paving the road to smart healthcare: The hybrid cloud comes in handy when the healthcare task needs a large amount of computational resources. By hybrid cloud, medical practitioners can analyze the genomics data and gain a deeper understanding of the causes of breast and ovarian cancer [153]. Moreover, computing and data resources provided by hybrid cloud can be exploited to run AI algorithms [154].…”

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

“…• Paving the road to smart healthcare: The hybrid cloud comes in handy when the healthcare task needs a large amount of computational resources. By hybrid cloud, medical practitioners can analyze the genomics data and gain a deeper understanding of the causes of breast and ovarian cancer [151]. Moreover, computing resources and data resources provided by hybrid cloud can be exploited to run AI algorithms [152].…”

Performance-efficient distributed transfer and transformation of big spatial histopathology datasets in the cloud

“…Then, a transformation container is launched for each list (Lines 8-10). The only difference in the algorithm of Transformation Containers is that now they have a list of files to transfer/transform instead of a single file (Lines [12][13][14][15][16][17][18][19][20][21][22].…”

“…The second set of challenges can result from the architecture of the cloud. Although clouds can provide tremendous opportunities for a plethora of biomedical applications [16][17][18][19][20][21][22], black box object storage systems (e.g., AWS S3) and poor cloud networks are the main reasons analytics applications perform poorly [23]. These studies usually deal with smaller size image formats such as MRIs, microarrays, pCTs and do not come across with the problems presented by extremely large WSI datasets.…”