Get your complex data labeled

Do you produce complex, large-scale data? Have you considered supervised machine learning as a powerful approach to mine that data? You’ll need a labeled reference set to train your algorithm.

The computational pipelines underlying SciSwipe process your complex data to simple images. We distribute these images to managed crowds and convert crowd actions into labeled data. You will be able to use this reference set to train a machine learning algorithm. The trained algorithm can infer new data, harnessing the power of supervised machine learning to predict, prevent or cure disease.

Target discovery at an unprecedented depth

The low signal-to-noise ratio in omics data has vastly limited the applicability of computational approaches and the depth at which the data is analyzed, particularly in situations where no prior information is available.

SciSwipe detects signals hidden in omics data by synergistically leveraging the human innate ability to visually detect patterns and the speed and scalability of computational algorithms. Increasing our ability to unmask true signals in noisy data dramatically advances the study of large-scale biological datasets available in the life sciences sector, improving our understanding of fundamental biological mechanisms in disease and enabling potential cures.

Method- and platform-independent haplotyping

We genotype or haplotype based on NGS or Third Generation Sequencing (TGS) data. We automate the process to produce results at high confidence and at scale. Our algorithms are equipped to handle targeted and whole-genome sequencing data from different sequencing platforms.

A secure data management workflow

We provide a dedicated, cloud-based platform for our services. In-house, accredited expertise enables us to design and implement robust and fully disclosed security and privacy measures that meet best practices in industry.