| 1. |
- Clark, Andrew G., et al.
(författare)
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Evolution of genes and genomes on the Drosophila phylogeny
- 2007
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 450:7167, s. 203-218
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Tidskriftsartikel (refereegranskat)abstract
- Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.
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| 2. |
- Wiedmann, Mareike M., et al.
(författare)
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Development of Cell-Permeable, Non-Helical Constrained Peptides to Target a Key Protein-Protein Interaction in Ovarian Cancer
- 2017
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Ingår i: Angewandte Chemie International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 56:2, s. 524-529
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Tidskriftsartikel (refereegranskat)abstract
- There is a lack of current treatment options for ovarian clear cell carcinoma (CCC) and the cancer is often resistant to platinum-based chemotherapy. Hence there is an urgent need for novel therapeutics. The transcription factor hepatocyte nuclear factor 1 beta (HNF1 beta) is ubiquitously overexpressed in CCC and is seen as an attractive therapeutic target. This was validated through shRNA-mediated knockdown of the target protein, HNF1 beta, in five high-and low-HNF1 beta-expressing CCC lines. To inhibit the protein function, cellpermeable, non-helical constrained proteomimetics to target the HNF1 beta-importin a protein-protein interaction were designed, guided by X-ray crystallographic data and molecular dynamics simulations. In this way, we developed the first reported series of constrained peptide nuclear import inhibitors. Importantly, this general approach may be extended to other transcription factors.
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| 3. |
- Buddenkotte, Thomas, et al.
(författare)
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Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation
- 2023
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Ingår i: Computers in Biology and Medicine. - : Elsevier Ltd. - 0010-4825 .- 1879-0534. ; 163
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Tidskriftsartikel (refereegranskat)abstract
- Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human–machine collaboration.
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