@article{MostosiSchindelinKollmannsbergeretal.2020,
  author    = {Mostosi, Philipp and Schindelin, Hermann and Kollmannsberger, Philip and Thorn, Andrea},
  title     = {Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps},
  series = {Angewandte Chemie International Edition},
  volume    = {59},
  journal   = {Angewandte Chemie International Edition},
  number    = {35},
  doi       = {10.1002/anie.202000421},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-214763},
  pages     = {14788 -- 14795},
  year      = {2020},
  abstract  = {In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de-novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo-EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main-chain placement. Due to its high recall and precision rates of 95.1 \% and 80.3 \%, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP-EM suite.},
  language  = {en}
}
@article{SahlolKollmannsbergerEwees2020,
  author    = {Sahlol, Ahmed T. and Kollmannsberger, Philip and Ewees, Ahmed A.},
  title     = {Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features},
  series = {Scientific Reports},
  volume    = {10},
  journal   = {Scientific Reports},
  number    = {1},
  doi       = {10.1038/s41598-020-59215-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229398},
  year      = {2020},
  abstract  = {White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.},
  language  = {en}
}
@article{PaulKollmannsberger2020,
  author    = {Paul, Torsten Johann and Kollmannsberger, Philip},
  title     = {Biological network growth in complex environments: A computational framework},
  series = {PLoS Computational Biology},
  volume    = {16},
  journal   = {PLoS Computational Biology},
  number    = {11},
  doi       = {10.1371/journal.pcbi.1008003},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231373},
  year      = {2020},
  abstract  = {Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function. Author summary We present a novel modeling approach and computational implementation to better understand the development of spatial biological networks under the influence of external signals. Our tool allows us to study the relationship between local biological growth parameters and the emerging macroscopic network function using simulations. This computational approach can generate plausible network graphs that take local feedback into account and provide a basis for comparative studies using graph-based methods.},
  language  = {en}
}