As the amount of digital data grows exponentially in all industries and sectors, a paramount challenge is the extraction of valuable knowledge from data and images. Our expertise in large-scale quantitative analysis and predictive models allows helping our partners in a broad range of domains, where the complexity and high dimension of the data require the employment of intelligent systems for automating the extraction of quantitative and meaningful information. Through the employment of suitable statistical and visualization tools we can help you represent and understand the information contained in:
- medical images
- 'omics data and other high-throughput biomedical data
- virtually any type of complex and high-dimensional data
Take a deeper look at our technology to better understand how our know-how can optimally address your technological issues.
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Medical imaging is gaining more and more importance from diagnosis, to treatment and prevention. This trend is making ever more pressing the need of new technologies helping physicians to cope with the growing flow of information and exploit it for improving our lives. The development of computer-aided detection and diagnosis systems, that are now of common use in some specialties, is addressing exactly this need.
Thanks to our expertise in automated image analysis and machine learning techniques, we can provide innovative solutions capable of extracting quantitative information from images acquired through, e.g. magnetic resonance or ultrasound.
'Omics data is a short cut for a number of measurements obtained with high-throughput technologies. They include proteomics (e.g. mass spectrometry measurements) and genomics (e.g. microarray, next generation sequencing) data among others. The analysis of ‘omics data has gained a central role in understanding biological processes, and involves a number of issues ranging from basic variable selection to the problem of devising statistical and visual tools to interpret and understand the biological meaning of the selected variables, from statistical inference to the problem of exploiting the available prior knowledge.
Our expertise in machine learning, allows us to propose reliable statistical tools that work in the typical -omics scenario of a small number of samples represented in a high dimensional space and that are able to capture the complex interactions among molecular entities.