Improving deconvolution methods in biology through open innovation competitions: an application to the connectivity map.

MOTIVATION: Do machine learning methods improve standard deconvolution techniques for gene expression data? This article uses a unique new dataset combined with an open innovation competition to evaluate a wide range of approaches developed by 294 competitors from 20 countries. The competition's objective was to address a deconvolution problem critical to analyzing genetic perturbations from the Connectivity Map. The issue consists of separating gene expression of individual genes from raw measurements obtained from gene pairs. We evaluated the outcomes using ground-truth data (direct measurements for single genes) obtained from the same samples. RESULTS: We find that the top-ranked algorithm, based on random forest regression, beat the other methods in accuracy and reproducibility; more traditional gaussian-mixture methods performed well and tended to be faster, and the best deep learning approach yielded outcomes slightly inferior to the above methods. We anticipate researchers in the field will find the dataset and algorithms developed in this study to be a powerful research tool for benchmarking their deconvolution methods and a resource useful for multiple applications. AVAILABILITY AND IMPLEMENTATION: The data is freely available at clue.io/data (section Contests) and the software is on GitHub at https://github.com/cmap/gene_deconvolution_challenge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advancing computational biology and bioinformatics research through open innovation competitions.

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

Discovery of a competitive apelin receptor (APJ) antagonist.

The apelin receptor (APJ) is a class A G-protein-coupled receptor (GPCR) and is a putative target for the treatment of cardiovascular and metabolic diseases. Apelin-13 (NH2-QRPRLSHKGPMPF-COOH) is a vasoactive peptide and one of the most potent endogenous inotropic agents identified to date. We report the design and discovery of a novel APJ antagonist. By using a bivalent ligand approach, we have designed compounds with two 'affinity' motifs and a short series of linker groups with different conformational and non-bonded interaction properties. One of these, cyclo(1-6)CRPRLC-KH-cyclo(9-14)CRPRLC is a competitive antagonist at APJ. Radioligand binding in CHO cells transfected with human APJ gave a K(i) value of 82 nM, competition binding in human left ventricle gave a K(D) value of 3.2 µM, and cAMP accumulation assays in CHO-K1-APJ cells gave a K(D) value of 1.32 µM.