In this article I would like to draw attention to reproducibility of results in biomarker discovery, focusing on the statistical perspective of the problem. Admittedly, reproducibility is a desired property of real markers. However, the important relationship between reproducibility requirement and the stability of statistical feature selection methods is not commonly known yet. So, let me try to clear things up a little...

A need for reproducibility in biomarker discovery

Even though the clinical potential of proteomics and metabolomics in biomarker discovery seems to be high, a non-reproducibility of detected (putative) biomarkers remains the main obstacle. Biomarkers identified by different research groups or even results based on different experiments conducted in the same lab often differ markedly. Hence, there is an emerging need for efficient statistical methods that will address issues of reproducibility and increase our confidence in discovered markers.

Marker reproducibility and feature selection stability

From the statistical perspective, the discovery of biomarkers from high-throughput 'omics' data means searching for the most discriminating features (e.g. features discriminating healthy from disease samples). Such task is usually referred to as feature selection (a good review of feature selection techniques in bioinformatics is given e.g. in [1]).

Hanna Kamińska has just started her Ph.D. studies during which she is going to work on the protein identification algorithms for modern tandem mass spectrometry solutions.

The aim of her studies is to improve the algorithms applied to database protein identification. Hanna's Ph.D. thesis advisor, Prof. Roman Zubarev from Karolinska Intitutet, is going to help her achieve that goal. The collaboration has already started and we are waiting impatiently for the first results.

We have released our new version of our products for analyzing MALDI and LC-MS data. We decided to change its name to Spectrolyzer to make it better reflect our focus on making best software for analyzing spectra.

There’s quite a lot of changes from the previous version, you can find all of them in the release notes. However I’d like to focus on two major improvements which we hope will make your life easier.

I have started collaboration with prof. Roman Zubarev on a project related to biomarker discovery for Alzheimer's disease.

Selected preliminary results (obtained using MedicWave software) have already been presented in Finland during FP7 Predict AD Workshop in June, 2011.