Big Data Analytics for Genomic Medicine

He, Karen Y.; Ge, Dongliang; He, Max M.

doi:10.3390/ijms18020412

Cited by 130 publications

(83 citation statements)

References 113 publications

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“…349,350 This new, data-rich reality, deriving in large measure from the convergence of advanced technologies such as next-generation sequencing, nano-technology, and imaging with the molecular and clinical sciences, portends a future of precision medicine in which patients receive molecularly targeted therapies and individualised disease management. 351,352 However, the integration, interrogation, management, and leverage of the ever-increasing pace and volume of these multidimensional data resources present challenges that range from improving data quality and security to achieving required interoperability across the cancer enterprise. These problems are not unique to oncology.…”

Section: Big Data and Enhanced Data-sharingmentioning

confidence: 99%

“…Powerful tools such as next-generation sequencing are empowering oncologists to determine the molecular profile of individual patients. 351 However, the benefits of precision medicine remain to be proven for individual cancers and across the landscape of cancer types and subtypes. Similarly, the benefits of cancer immunotherapy can be profound for individual patients, but the challenge of identifying those patients is a major barrier to broad implementation.…”

Section: Big Data and Enhanced Data-sharingmentioning

confidence: 99%

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Future cancer research priorities in the USA: a Lancet Oncology Commission

Jaffee

Dang

Agus

et al. 2017

The Lancet Oncology

171

123

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We are in the midst of a technological revolution that is providing new insights into human biology and cancer. In this era of big data, we are amassing large amounts of information that is transforming how we approach cancer treatment and prevention. Enactment of the Cancer Moonshot within the 21st Century Cures Act in the USA arrived at a propitious moment in the advancement of knowledge, providing nearly US$2 billion of funding for cancer research and precision medicine. In 2016, the Blue Ribbon Panel (BRP) set out a roadmap of recommendations designed to exploit new advances in cancer diagnosis, prevention, and treatment. Those recommendations provided a high-level view of how to accelerate the conversion of new scientific discoveries into effective treatments and prevention for cancer. The US National Cancer Institute is already implementing some of those recommendations. As experts in the priority areas identified by the BRP, we bolster those recommendations to implement this important scientific roadmap. In this Commission, we examine the BRP recommendations in greater detail and expand the discussion to include additional priority areas, including surgical oncology, radiation oncology, imaging, health systems and health disparities, regulation and financing, population science, and oncopolicy. We prioritise areas of research in the USA that we believe would accelerate efforts to benefit patients with cancer. Finally, we hope the recommendations in this report will facilitate new international collaborations to further enhance global efforts in cancer control.

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Section: Big Data and Enhanced Data-sharingmentioning

confidence: 99%

Section: Big Data and Enhanced Data-sharingmentioning

confidence: 99%

Future cancer research priorities in the USA: a Lancet Oncology Commission

Jaffee

Dang

Agus

et al. 2017

The Lancet Oncology

171

123

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“…In high‐income settings, big data are currently the focus of genomewide data analysis, developing personal omics profiles and individualized oncology treatment. Meanwhile machine‐learning algorithms are being developed to help deliver care, inform health policy and reduce waste.…”

Section: Discussionmentioning

confidence: 99%

Systematic review of the use of big data to improve surgery in low- and middle-income countries

Knight

Ots

Maimbo

et al. 2019

British Journal of Surgery

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Background Technological advances have led to the generation of large amounts of data, both in surgical research and practice. Despite this, it is unclear how much originates in low‐ and middle‐income countries (LMICs) and what barriers exist to the use of such data in improving surgical care. The aim of this review was to capture the extent and impact of programmes that use large volumes of patient data on surgical care in LMICs. Methods A PRISMA‐compliant systematic literature review of PubMed, Embase and Google Scholar was performed in August 2018. Prospective studies collecting large volumes of patient‐level data within LMIC settings were included and evaluated qualitatively. Results A total of 68 studies were included from 71 LMICs, involving 708 032 patients. The number of patients in included studies varied widely (from 335 to 428 346), with 25 reporting data on 3000 or more LMIC patients. Patient inclusion in large‐data studies in LMICs has increased dramatically since 2015. Studies predominantly involved Brazil, China, India and Thailand, with low patient numbers from Africa and Latin America. Outcomes after surgery were commonly the focus (33 studies); very few large studies looked at access to surgical care or patient expenditure. The use of large data sets specifically to improve surgical outcomes in LMICs is currently limited. Conclusion Large volumes of data are becoming more common and provide a strong foundation for continuing investigation. Future studies should address questions more specific to surgery.

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“…Omics research is currently tightly bound to the rising number of patient records in medical sciences and the application of Next Generation Sequencing (NGS) technologies ( [7]). The cumulative amount of genomic datasets in the SRA (Sequence Read Archive) database increased to 9000 TB in 2017 [8] and to over 13000 TB in 2020 [9].…”

Section: Introductionmentioning

confidence: 99%

BigMPI4py: Python module for parallelization of Big Data objects

Araúzo-Bravo

2019

Preprint

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Big Data analysis is a discipline with a growing number of areas where huge amounts of data is extracted and analyzed. Parallelization in Python integrates Message Passing Interface via mpi4py module. Since mpi4py does not support parallelization of objects greater than 2 31 bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and also uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and HPC systems. We validated BigMPI4py on whole genome bisulfite sequencing (WGBS) DNA methylation ENCODE data of 59 samples from 27 human tissues. We categorized them on the three germ layers and developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. We observed a differentiation of the germ layers, and a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores. BigMPI4py is available at https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis

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Big Data Analytics for Genomic Medicine

Cited by 130 publications

References 113 publications

Future cancer research priorities in the USA: a Lancet Oncology Commission

Future cancer research priorities in the USA: a Lancet Oncology Commission

Systematic review of the use of big data to improve surgery in low- and middle-income countries

BigMPI4py: Python module for parallelization of Big Data objects

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