@article{CohenHershcovitchTarazetal.2023,
  author    = {Cohen, Sarel and Hershcovitch, Moshik and Taraz, Martin and Kissig, Otto and Issac, Davis and Wood, Andrew and Waddington, Daniel and Chin, Peter and Friedrich, Tobias},
  title     = {Improved and optimized drug repurposing for the SARS-CoV-2 pandemic},
  series = {PLoS one},
  volume    = {18},
  journal   = {PLoS one},
  number    = {3},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0266572},
  pages     = {13},
  year      = {2023},
  abstract  = {The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware-we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.},
  language  = {en}
}
@article{ChandranIssacLaurietal.2022,
  author    = {Chandran, Sunil L. and Issac, Davis and Lauri, Juho and van Leeuwen, Erik Jan},
  title     = {Upper bounding rainbow connection number by forest number},
  series = {Discrete mathematics},
  volume    = {345},
  journal   = {Discrete mathematics},
  number    = {7},
  publisher = {Elsevier},
  address   = {Amsterdam [u.a.]},
  issn      = {0012-365X},
  doi       = {10.1016/j.disc.2022.112829},
  pages     = {22},
  year      = {2022},
  abstract  = {A path in an edge-colored graph is rainbow if no two edges of it are colored the same, and the graph is rainbow-connected if there is a rainbow path between each pair of its vertices. The minimum number of colors needed to rainbow-connect a graph G is the rainbow connection number of G, denoted by rc(G).\& nbsp;A simple way to rainbow-connect a graph G is to color the edges of a spanning tree with distinct colors and then re-use any of these colors to color the remaining edges of G. This proves that rc(G) <= |V (G)|-1. We ask whether there is a stronger connection between tree-like structures and rainbow coloring than that is implied by the above trivial argument. For instance, is it possible to find an upper bound of t(G)-1 for rc(G), where t(G) is the number of vertices in the largest induced tree of G? The answer turns out to be negative, as there are counter-examples that show that even c .t(G) is not an upper bound for rc(G) for any given constant c.\& nbsp;In this work we show that if we consider the forest number f(G), the number of vertices in a maximum induced forest of G, instead of t(G), then surprisingly we do get an upper bound. More specifically, we prove that rc(G) <= f(G) + 2. Our result indicates a stronger connection between rainbow connection and tree-like structures than that was suggested by the simple spanning tree based upper bound.},
  language  = {en}
}