Stefanie Friedrich

Stefanie Friedrich


Visa sidan på svenska
Works at Department of Biochemistry and Biophysics
Visiting address Tomtebodavägen 23, 171 65 Solna
Postal address Institutionen för biokemi och biofysik 106 91 Stockholm


Research group: Erik Sonnhammer


A selection from Stockholm University publication database
  • 2018. Emelie Berglund (et al.). Nature Communications 9

    Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.

  • 2016. Jing Yan, Stefanie Friedrich, Lukasz Kurgan. Briefings in Bioinformatics 17 (1), 88-105

    Motivated by the pressing need to characterize protein-DNA and protein-RNA interactions on large scale, we review a comprehensive set of 30 computational methods for high-throughput prediction of RNA- or DNA-binding residues from protein sequences. We summarize these predictors from several significant perspectives including their design, outputs and availability. We perform empirical assessment of methods that offer web servers using a new benchmark data set characterized by a more complete annotation that includes binding residues transferred from the same or similar proteins. We show that predictors of DNA-binding (RNA-binding) residues offer relatively strong predictive performance but they are unable to properly separate DNA- from RNA-binding residues. We design and empirically assess several types of consensuses and demonstrate that machine learning (ML)-based approaches provide improved predictive performance when compared with the individual predictors of DNA-binding residues or RNA-binding residues. We also formulate and execute first-of-its-kind study that targets combined prediction of DNA- and RNA-binding residues. We design and test three types of consensuses for this prediction and conclude that this novel approach that relies on ML design provides better predictive quality than individual predictors when tested on prediction of DNA- and RNA-binding residues individually. It also substantially improves discrimination between these two types of nucleic acids. Our results suggest that development of a new generation of predictors would benefit from using training data sets that combine both RNA- and DNA-binding proteins, designing new inputs that specifically target either DNA- or RNA-binding residues and pursuing combined prediction of DNA- and RNA-binding residues.

  • 2015. Stefanie Friedrich, Hercules Dalianis. Sixth International Workshop on Health Text Mining and Information Analysis (LOUHI), 121-130

    A method to find adverse drug reactions in electronic health records written in Swedish is presented. A total of 14,751 health records were manually classified into four groups. The records are normalised by pre-processing using both dic- tionaries and manually created word lists. Three different supervised machine learning algorithm were used to find the best results; decision tree, random forest and LibSVM. The best performance on a test dataset was with LibSVM obtaining a pre- cision of 0.69 and a recall of 0.66, and a F-score of 0.67. Our method found 865 of 981 true positives (88.2%) in a 3-class dataset which is an improvement of 49.5% over previous approaches.

Show all publications by Stefanie Friedrich at Stockholm University

Last updated: September 13, 2018

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