Ten Simple Rules for a Community Computational Challenge
Publication Date
April 23, 2015
Journal
PLOS Computational Biology
Authors
Iddo Friedberg, Mark N. Wass, Sean D. Mooney & Predrag Radivojac
Volume
11
Issue
4
Pages
e1004150
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004150
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004150
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/25906249
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408123
Europe PMC
http://europepmc.org/abstract/MED/25906249
Scopus
84929469657
Mendeley
http://www.mendeley.com/research/ten-simple-rules-community-computational-challenge
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Mendeley | Further Information

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With better lens crafting came microscopes, and with them the discovery of living cells. In the last 40 years, advances in molecular biology, statistics, and computer science have ush-ered in the field of bioinformatics and the genomic era. Computational scientists enjoy developing new methods, and the community encourages them to do so. Indeed, the editorial guidelines for PLOS Computational Biology require manu-scripts to apply novel methods. However, it is often confusing to know which method to choose: which method is best? And, in this context, what does \" best \" mean? To help choose an appropriate method for a particular task, scientists often form communi-ty-based challenges for the unbiased evaluation of methods in a given field. These challenges help evaluate existing and novel methods, while helping to coalesce a community and leading to new ideas and collaborations. In computational biology, the first of these challenges was arguably the Critical Assess-ment of protein Structure Prediction, or CASP [1], whose goal is to evaluate methods for pre-dicting three-dimensional protein structure from amino acid sequence. The first CASP meeting was held in December of 1994, following a \" prediction period \" where members of the community were presented with protein amino acid sequences and asked to predict their three dimensional structures. The sequences that were chosen had recently been solved by X-ray crystallography but had not been not published or released until after the predictions from the community were made. Since the first CASP, we have seen many successful chal-lenges, including Critical Assessment of Function Annotation (CAFA) for protein function prediction [2], Critical Assessment of Genome Interpretation (CAGI) (for genome interpreta-tion) [3], Critical Assessment of Massive (originally \" Microarray \") Data Analysis (CAMDA) (for large-scale biological data) [4], BioCreative (for biomedical text mining) [5], the Assem-blathon (for sequence assembly), and the NCI-DREAM Challenges (for various biomedical challenges), amongst others [6]. Computational challenges also help solve new problems. While the original CASP experi-ment was developed to evaluate existing methods applied to current problems, other commu-nities often look at other areas for which there are no existing tools. 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Scopus | Further Information

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  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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