ReviewCancer informatics2014
Next generation distributed computing for cancer research.
Review in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.Gastroenterology · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in next generation sequencing (NGS) and mass spectrometry (MS) technologies have provided many new opportunities and angles for extending the scope of translational cancer research while creating tremendous challenges in data management and analysis. The resulting informatics challenge is invariably not amenable to the use of traditional computing models. Recent advances in scalable computing and associated infrastructure, particularly distributed computing for Big Data, can provide solutions for addressing these challenges. In this review, the next generation of distributed computing technologies that can address these informatics problems is described from the perspective of three key components of a computational platform, namely computing, data storage and management, and networking. A broad overview of scalable computing is provided to set the context for a detailed description of Hadoop, a technology that is being rapidly adopted for large-scale distributed computing. A proof-of-concept Hadoop cluster, set up for performance benchmarking of NGS read alignment, is described as an example of how to work with Hadoop. Finally, Hadoop is compared with a number of other current technologies for distributed computing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.