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Competitions & Challenges
Contest 2: Algorithm Speed Challenge

The Connectivity Map (CMap) team will launch the second in a series of crowdsourcing contests on Thursday, January 26. A collaboration with the Crowd Innovation Lab at Harvard Business School and the Topcoder platform, with sponsorship from the Kraft Family Foundation, the two-week challenge aims to improve the speed of the CMap query algorithm.

For insight into the practical applications of this challenge, consider that the CMap L1000 dataset is fast-approaching 2 million profiles and will continue to grow rapidly. As the size of the dataset increases, improving the speed of the query algorithm is paramount to researchers using the CMap resource. Enter the competition and compete for a share of a $20,000 prize purse by registering on the TopCoder platform.

For more information...

Overview of the Connectivity Map

CMap enables the discovery of functional connections between drugs, genes and diseases through the generation and analysis of gene expression signatures, where each signature represents the transcriptional response of human cells to chemical or genetic perturbation. To identify connections, a researcher poses a biological question in the form of a “query” comprised of a list of genes of interest. The query algorithm then searches the CMap database to identify signatures that are most similar to the user's input. By using this algorithm and the Connectivity Map dataset, researchers have uncovered novel biological relationships and generated hypotheses for the development of new therapeutics.

The Challenge

The CMap L1000 assay quantifies the responses of 10,174 genes to an experimental perturbation. Due to recent technological improvements, it has become possible to massively scale-up data generation. As a result, the CMap matrix has grown to 476,251 signatures and this number is expected to continue to increase rapidly. The goal of the contest is to improve the speed of the query algorithm, as this has great practical importance for researchers using CMap

Contestants are provided with a dataset of almost half a million CMap signatures, exemplar queries and a detailed description of the query algorithm along with source code. Contestants are tasked with submitting code implementing this algorithm. Submissions are scored for speed of execution, and at the completion of the contest, entries will be evaluated using a “hold-out” collection of queries to determine the winners.

Prizes and Participation

In addition to helping advance biomedical research, the winners were awarded cash prizes, with a total purse of $20,000. Compete in this challenge by registering on the TopCoder contest page.

Contest Closed

Jan 26 - Feb 16, 2017
$20,000 prize
See Details
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Upcoming contests

Spring 2017
Contest3
Details TBA

Past contests

June 28, 2016 - July 19, 2016
Contest1: Inference Challenge
The goal of this contest was to maximize the accuracy of the inferred gene expression values used by the Connectivity Map, while minimizing the number of the measured gene expressions. Results of this contest expanded research horizons for computational biologists and scientists who seek to find drugs that cure diseases.
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Contest Details
CMap utilizes a novel, high-throughput gene expression profiling technology to generate gene expression profiles at scale. The crux of this approach is that instead of measuring all ~20,000 genes in the human genome, CMap measures a select subset of approximately 1,000 genes and uses these “landmark” gene measurements to computationally infer a large portion of the remainder. The current algorithm is effective but imperfect, and improving the imputation methods will have an immediate impact on the quality of data and the biologically meaningful connections that can be discovered. With this in mind, we have designed our first contest to stimulate the exploration of new and improved inference methods.
Prizes and Participation
Several of the top contestants achieved a notable improvement over the current inference model. An improved version of the model, based on the results of this contest, is being prepared for addition to clue.io pending further validation (expected Summer 2017). Click here for more details on the results, or click here to see the final leaderboard on the TopCoder site.

About CMap

The Connectivity Map (CMap) is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive, small molecules and pattern-matching algorithms. When these elements are brought together, the results enable the discovery of functional connections between drugs, genes and diseases through the transitory feature of common gene-expression changes. For more on CMap, click here.