The increasing relevance of Big Data in modern life sciences creates one of the grand challenges of our times. The analysis of large data sets is consequently emerging as one of the scientific key techniques in the post-genomic era. Recently, the growing need for the analysis of massive data has further been accelerated by the advent and fast development of high-throughput next-generation sequencing technologies and the necessarily increasing cohort size of biomedical studies. Still, the data analysis bottleneck limits the rate at which technological advances in genome-scale experimental platforms can provide new medical and biological insights.
CAMDA has a track record as a well-recognized annual conference going back to the year 2000. It soon received considerable attention from high impact journals like Nature and recently in Nature Communications (2019) as well as was featured in an editorial in Nature Methods in 2008. Recently called the 'Olympics for Genomics', this allusion indicates the ambitious and wide-ranging nature of the contest. The meeting has regularly been supported by high-profile organizations like the FDA and NIST.
CAMDA focuses on the analysis of massive data in the life sciences. It introduces and evaluates new approaches and solutions to the Big Data challenge. The conference presents new techniques in the field of bioinformatics, data analysis, and statistics for the handling and processing of large data sets, the combination of multiple data sources, and effective computational inference.
An essential part of CAMDA is its open-ended data analysis challenge of complex data sets, often featuring novel technological platforms, exceptionally large cohorts, and heterogeneous data sources and types. Academic and industrial researchers worldwide alike are invited to take the CAMDA challenge. Accepted contributions are presented in short talks, and the results of analyses are discussed and compared at the CAMDA conference. Both contestants and other interested researchers are welcome at the meeting. Posters can provide an additional opportunity of presenting and discuss work.
The scope of CAMDA Research Topic covers the challenges announced annually on the CAMDA webpage camda.info
• The Disease Maps to modelling COVID-19 Challenge provides expert-curated molecular mechanistic maps for COVID-19 and large-scale expression profiles. Combine them to expand our understanding of COVID-19 mechanisms.
• The Literature AI for Drug-Induced Liver Injury Challenge provides biomedical publications curated by FDA experts on DILI. Build their digital twin to distinguish positives from a challenging negative set.
• The Metagenomic Phage Forensics of Anti-Microbial Resistance Challenge features diverse meta-genomics profiles from urban areas. Track emerging AMR and its spread by gene transfer through phages.
• The Hi-Res Cancer Data Integration Challenge presents clinical and matched molecular profiles, with reading level data for individual genomes as well as expression estimated from novel genomic regions. Explore non-standard genomes and splicing events for a better prognosis.
This is contest-specific with a set of submission requirements. In particular:
- All research submitted to CAMDA must be previously unpublished original work intended for publication, including procedures and results.
- Challenge data set embargo: Any challenge data that is not already in the public domain remains exclusive for participants in the contest until conference presentation. This means that no publication of your results is allowed before the CAMDA conference.
- Once research has been accepted for publication at CAMDA, however, dissemination by pre-print servers is fine and encouraged.
- Research introducing data sets outside the set CAMDA challenges need to put these data in the context of the challenges and make both raw and derived data publicly available if the submitted work is accepted.
- Methods research needs to include at least two independent types of validation, such as a benchmark on simulated data with known truth, a benchmark on real-world data with built-in truths, or an application to real-world data with critical biological/medical interpretation.
- Researchers introducing novel computational approaches must make their procedure available to others (e.g., source code or commercial demo), and a publication of source code is strongly encouraged.
The increasing relevance of Big Data in modern life sciences creates one of the grand challenges of our times. The analysis of large data sets is consequently emerging as one of the scientific key techniques in the post-genomic era. Recently, the growing need for the analysis of massive data has further been accelerated by the advent and fast development of high-throughput next-generation sequencing technologies and the necessarily increasing cohort size of biomedical studies. Still, the data analysis bottleneck limits the rate at which technological advances in genome-scale experimental platforms can provide new medical and biological insights.
CAMDA has a track record as a well-recognized annual conference going back to the year 2000. It soon received considerable attention from high impact journals like Nature and recently in Nature Communications (2019) as well as was featured in an editorial in Nature Methods in 2008. Recently called the 'Olympics for Genomics', this allusion indicates the ambitious and wide-ranging nature of the contest. The meeting has regularly been supported by high-profile organizations like the FDA and NIST.
CAMDA focuses on the analysis of massive data in the life sciences. It introduces and evaluates new approaches and solutions to the Big Data challenge. The conference presents new techniques in the field of bioinformatics, data analysis, and statistics for the handling and processing of large data sets, the combination of multiple data sources, and effective computational inference.
An essential part of CAMDA is its open-ended data analysis challenge of complex data sets, often featuring novel technological platforms, exceptionally large cohorts, and heterogeneous data sources and types. Academic and industrial researchers worldwide alike are invited to take the CAMDA challenge. Accepted contributions are presented in short talks, and the results of analyses are discussed and compared at the CAMDA conference. Both contestants and other interested researchers are welcome at the meeting. Posters can provide an additional opportunity of presenting and discuss work.
The scope of CAMDA Research Topic covers the challenges announced annually on the CAMDA webpage camda.info
• The Disease Maps to modelling COVID-19 Challenge provides expert-curated molecular mechanistic maps for COVID-19 and large-scale expression profiles. Combine them to expand our understanding of COVID-19 mechanisms.
• The Literature AI for Drug-Induced Liver Injury Challenge provides biomedical publications curated by FDA experts on DILI. Build their digital twin to distinguish positives from a challenging negative set.
• The Metagenomic Phage Forensics of Anti-Microbial Resistance Challenge features diverse meta-genomics profiles from urban areas. Track emerging AMR and its spread by gene transfer through phages.
• The Hi-Res Cancer Data Integration Challenge presents clinical and matched molecular profiles, with reading level data for individual genomes as well as expression estimated from novel genomic regions. Explore non-standard genomes and splicing events for a better prognosis.
This is contest-specific with a set of submission requirements. In particular:
- All research submitted to CAMDA must be previously unpublished original work intended for publication, including procedures and results.
- Challenge data set embargo: Any challenge data that is not already in the public domain remains exclusive for participants in the contest until conference presentation. This means that no publication of your results is allowed before the CAMDA conference.
- Once research has been accepted for publication at CAMDA, however, dissemination by pre-print servers is fine and encouraged.
- Research introducing data sets outside the set CAMDA challenges need to put these data in the context of the challenges and make both raw and derived data publicly available if the submitted work is accepted.
- Methods research needs to include at least two independent types of validation, such as a benchmark on simulated data with known truth, a benchmark on real-world data with built-in truths, or an application to real-world data with critical biological/medical interpretation.
- Researchers introducing novel computational approaches must make their procedure available to others (e.g., source code or commercial demo), and a publication of source code is strongly encouraged.
