Collaboration with Karolinska Institutet (2008)

Karolinska Institutet is one of Europe's largest medical universities. It is also Sweden´s largest centre for medical training and research. Our mission is to improve the health of mankind through research and education.

Scientists from Karolinska Institutet

The scientists who developed ProSpect are:

  • Yudi Pawitan, Professor, PhD (Karolinska Institutet, Sweden)
  • Chuen Seng Tan, PhD (Karolinska Institutet, Sweden)
  • Alexander Ploner, PhD (Karolinska Institutet, Sweden)
  • Andreas Quandt, PhD student (Karolinska Institutet, Sweden)

ProSpect

MedicWave collaborates with a research group from Karolinska Institutet in Sweden. During our collaboration we received a unique peak extraction software with algorithms called ProSpect. ProSpect is part of MedicWave's MBS™ software.

Peak detection is a key step in the analysis of SELDI-TOF-MS spectra, but the current default method has low specificity and poor peak annotation. To improve data quality, scientists still have to validate the identified peaks visually, a tedious and time-consuming process, especially for large data sets. Hence, there is a genuine need for methods that minimize manual validation. We have previously reported a multi-spectral signal detection method, called RS for 'region of significance', with improved specificity. Here we extend it to include a peak quantification algorithm based on annotated regions of significance (ARS). For each spectral region flagged as significant by RS, we first identify a dominant spectrum for determining the number of peaks and the m/z region of these peaks. From each m/z region of peaks, a peak template is extracted from all spectra via the principal component analysis. Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model. We have evaluated the ARS algorithm on patient samples from a clinical study. Comparison with the Ciphergen method shows that ARS (i) inherits the superior specificity of RS, and (ii) gives more accurate peak annotations than the standard method. In conclusion, we find that ARS alleviates the main problems in the preprocessing of SELDI-TOF spectra.

Click here to learn more about ProSpect.

Publications concerning ProSpect

Open for collaboration

MedicWave develops state-of-the-art algorithms for data mining and machine learning in collaboration with renowned researcher from around the world. These algorithms are implemented into our bioinformatics products and used for differential proteomics studies, quantification and validation of regulated proteins, and the discovery of biomarkers.

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