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arXiv 2023年

作者： Despotovic, Vladimir Kim, Sang-Yoon Hau, Ann-Christin Kakoichankava, Aliaksandra Klamminger, Gilbert Georg Borgmann, Felix Bruno Kleine Frauenknecht, Katrin B.M. Mittelbronn, Michel Nazarov, Petr V. Bioinformatics Platform Data Integration and Analysis Unit Luxembourg Institute of Health Strassen Luxembourg Dr. Senckenberg Institute of Neurooncology University Hospital Frankfurt Frankfurt am Main Germany Edinger Institute Institute of Neurology Goethe University Frankfurt am Main Germany Frankfurt Cancer Institute Goethe University Frankfurt am Main Germany University Cancer Center Frankfurt Frankfurt am Main Germany University Hospital Goethe University Frankfurt am Main Germany Laboratoire national de santé National Center of Pathology Dudelange Luxembourg Multi-Omics Data Science group Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre of Neuropathology Dudelange Luxembourg Klinik für Frauenheilkunde Geburtshilfe und Reproduktionsmedizin Saarland University Homburg Germany Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre for Systems Biomedicine University of Luxembourg Belval Luxembourg Department of Life Sciences and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist’s efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas;thus, providing insights to pathologists concerning the most informative parts of the WSI. © 2023, CC BY-NC-ND.

关键词： Tumors

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Fast and accurate calculation of protein depth by Euclidean distance transform

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17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Xu, Dong Li, Hua Zhang, Yang Bioinformatics and Systems Biology Program Sanford-Burnham Medical Research Institute United States Department of Computational Medicine and Bioinformatics University of Michigan United States Integration Application Center Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9783642371943

The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation. © 2013 Springer-Verlag.

关键词： Proteins

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is-rSNP: a novel technique for in silico regulatory SNP detection

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BMC bioinformatics 2010年第10期11卷 1-1页

作者： Geoff Macintyre James Bailey Adam Kowalczyk Izhak Haviv Department of Computer Science and Software Engineering The University of Melbourne Victoria 3010 Australia NICTA Victoria Research Lab The University of Melbourne Victoria 3010 Australia Department of Biochemistry and Molecular Biology The University of Melbourne Victoria 3010 Australia Ian Potter Centre for Cancer Genomics and Predictive Medicine Peter MacCallum Cancer Centre East Melbourne Victoria Australia Bioinformatics and Systems Integration The Blood and DNA Profiling Facility Baker IDI heart and diabetes Institute 75 Commercial Rd Prahran 3004 Victoria Australia

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