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  1. Article: Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling-A Feasibility Study.

    Weißenborn, Sandra / Walther, Dirk

    Frontiers in plant science

    2017  Volume 8, Page(s) 1831

    Abstract: Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes ... ...

    Abstract Despite many developed experimental and computational approaches, functional gene annotation remains challenging. With the rapidly growing number of sequenced genomes, the concept of phylogenetic profiling, which predicts functional links between genes that share a common co-occurrence pattern across different genomes, has gained renewed attention as it promises to annotate gene functions based on presence/absence calls alone. We applied phylogenetic profiling to the problem of metabolic pathway assignments of plant genes with a particular focus on secondary metabolism pathways. We determined phylogenetic profiles for 40,960 metabolic pathway enzyme genes with assigned EC numbers from 24 plant species based on sequence and pathway annotation data from KEGG and Ensembl Plants. For gene sequence family assignments, needed to determine the presence or absence of particular gene functions in the given plant species, we included data of all 39 species available at the Ensembl Plants database and established gene families based on pairwise sequence identities and annotation information. Aside from performing profiling comparisons, we used machine learning approaches to predict pathway associations from phylogenetic profiles alone. Selected metabolic pathways were indeed found to be composed of gene families of greater than expected phylogenetic profile similarity. This was particularly evident for primary metabolism pathways, whereas for secondary pathways, both the available annotation in different species as well as the abstraction of functional association via distinct pathways proved limiting. While phylogenetic profile similarity was generally not found to correlate with gene co-expression, direct physical interactions of proteins were reflected by a significantly increased profile similarity suggesting an application of phylogenetic profiling methods as a filtering step in the identification of protein-protein interactions. This feasibility study highlights the potential and challenges associated with phylogenetic profiling methods for the detection of functional relationships between genes as well as the need to enlarge the set of plant genes with proven secondary metabolism involvement as well as the limitations of distinct pathways as abstractions of relationships between genes.
    Language English
    Publishing date 2017-10-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2711035-7
    ISSN 1664-462X
    ISSN 1664-462X
    DOI 10.3389/fpls.2017.01831
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online ; Thesis: Neuropsychiatrische Symptome bei Patienten mit Hepatitis C-Infektion ohne relevante Lebererkrankung im Langzeit-Follow-Up

    Braun, David [Verfasser] / Weißenborn, Karin [Akademischer Betreuer] / Dirks, Meike [Akademischer Betreuer]

    2024  

    Author's details David Braun ; Akademische Betreuer: Karin Weißenborn, Meike Dirks ; Klinik für Neurologie
    Keywords Medizin, Gesundheit ; Medicine, Health
    Subject code sg610
    Language German
    Publisher Bibliothek der Medizinischen Hochschule Hannover
    Publishing place Hannover
    Document type Book ; Online ; Thesis
    Database Digital theses on the web

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  3. Book ; Online: Colorization Transformer

    Kumar, Manoj / Weissenborn, Dirk / Kalchbrenner, Nal

    2021  

    Abstract: We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to ... ...

    Abstract We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to produce a low resolution coarse coloring of the grayscale image. Our architecture adopts conditional transformer layers to effectively condition grayscale input. Two subsequent fully parallel networks upsample the coarse colored low resolution image into a finely colored high resolution image. Sampling from the Colorization Transformer produces diverse colorings whose fidelity outperforms the previous state-of-the-art on colorising ImageNet based on FID results and based on a human evaluation in a Mechanical Turk test. Remarkably, in more than 60% of cases human evaluators prefer the highest rated among three generated colorings over the ground truth. The code and pre-trained checkpoints for Colorization Transformer are publicly available at https://github.com/google-research/google-research/tree/master/coltran

    Comment: ICLR 2021 Camera Ready. See https://openreview.net/forum?id=5NA1PinlGFu for more details
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-02-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Scaling Autoregressive Video Models

    Weissenborn, Dirk / Täckström, Oscar / Uszkoreit, Jakob

    2019  

    Abstract: Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models attempt to address these issues by ... ...

    Abstract Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models attempt to address these issues by combining sometimes complex, often video-specific neural network architectures, latent variable models, adversarial training and a range of other methods. Despite their often high complexity, these approaches still fall short of generating high quality video continuations outside of narrow domains and often struggle with fidelity. In contrast, we show that conceptually simple, autoregressive video generation models based on a three-dimensional self-attention mechanism achieve highly competitive results across multiple metrics on popular benchmark datasets for which they produce continuations of high fidelity and realism. Furthermore, we find that our models are capable of producing diverse and surprisingly realistic continuations on a subset of videos from Kinetics, a large scale action recognition dataset comprised of YouTube videos exhibiting phenomena such as camera movement, complex object interactions and diverse human movement. To our knowledge, this is the first promising application of video-generation models to videos of this complexity.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
    Subject code 004
    Publishing date 2019-06-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Axial Attention in Multidimensional Transformers

    Ho, Jonathan / Kalchbrenner, Nal / Weissenborn, Dirk / Salimans, Tim

    2019  

    Abstract: We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors. Existing autoregressive models either suffer from excessively large computational resource requirements for high ... ...

    Abstract We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors. Existing autoregressive models either suffer from excessively large computational resource requirements for high dimensional data, or make compromises in terms of distribution expressiveness or ease of implementation in order to decrease resource requirements. Our architecture, by contrast, maintains both full expressiveness over joint distributions over data and ease of implementation with standard deep learning frameworks, while requiring reasonable memory and computation and achieving state-of-the-art results on standard generative modeling benchmarks. Our models are based on axial attention, a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. Notably the proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. This semi-parallel structure goes a long way to making decoding from even a very large Axial Transformer broadly applicable. We demonstrate state-of-the-art results for the Axial Transformer on the ImageNet-32 and ImageNet-64 image benchmarks as well as on the BAIR Robotic Pushing video benchmark. We open source the implementation of Axial Transformers.

    Comment: 10 pages
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 629
    Publishing date 2019-12-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Comparison of 6 tests for diagnosing minimal hepatic encephalopathy and predicting clinical outcome: A prospective, observational study.

    Ehrenbauer, Alena F / Egge, Julius F M / Gabriel, Maria M / Tiede, Anja / Dirks, Meike / Witt, Jennifer / Wedemeyer, Heiner / Maasoumy, Benjamin / Weissenborn, Karin

    Hepatology (Baltimore, Md.)

    2024  

    Abstract: Background and aims: Current guidelines recommend the assessment for minimal HE in patients with liver cirrhosis. Various efforts were made to find tools that simplify the diagnosis. Here, we compare the 6 most frequently used tests for their validity ... ...

    Abstract Background and aims: Current guidelines recommend the assessment for minimal HE in patients with liver cirrhosis. Various efforts were made to find tools that simplify the diagnosis. Here, we compare the 6 most frequently used tests for their validity and their predictive value for overt hepatic encephalopathy (oHE), rehospitalization, and death.
    Approach and results: One hundred thirty-two patients with cirrhosis underwent the Portosystemic Encephalopathy-Syndrome-Test yielding the psychometric hepatic encephalopathy score (PHES), Animal Naming Test (ANT), Critical Flicker Frequency (CFF), Inhibitory Control Test (ICT), EncephalApp (Stroop), and Continuous Reaction Time Test (CRT). Patients were monitored for 365 days regarding oHE development, rehospitalization, and death. Twenty-three patients showed clinical signs of HE grade 1-2 at baseline. Of the remaining 109 neurologically unimpaired patients, 35.8% had abnormal PHES and 44% abnormal CRT. Percentage of abnormal Stroop (79.8% vs. 52.3%), ANT (19.3% vs. 51.4%), ICT (28.4% vs. 36.7%), and CFF results (18.3% vs. 25.7%) changed significantly when adjusted norms were used for evaluation instead of fixed cutoffs. All test results correlated significantly with each other ( p <0.05), except for CFF. During follow-up, 24 patients developed oHE, 58 were readmitted to the hospital, and 20 died. Abnormal PHES results were linked to oHE development in the multivariable model. No other adjusted test demonstrated predictive value for any of the investigated endpoints.
    Conclusions: Where applicable, the diagnosis of minimal HE should be made based on adjusted norm values for the tests, exclusively. The minimal HE tests cannot be equated with one another and have an overall limited value in predicting clinical outcomes.
    Language English
    Publishing date 2024-02-13
    Publishing country United States
    Document type Journal Article
    ZDB-ID 604603-4
    ISSN 1527-3350 ; 0270-9139
    ISSN (online) 1527-3350
    ISSN 0270-9139
    DOI 10.1097/HEP.0000000000000770
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Differentiable Patch Selection for Image Recognition

    Cordonnier, Jean-Baptiste / Mahendran, Aravindh / Dosovitskiy, Alexey / Weissenborn, Dirk / Uszkoreit, Jakob / Unterthiner, Thomas

    2021  

    Abstract: Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select ... ...

    Abstract Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the most relevant parts of the input to efficiently process high resolution images. Our method may be interfaced with any downstream neural network, is able to aggregate information from different patches in a flexible way, and allows the whole model to be trained end-to-end using backpropagation. We show results for traffic sign recognition, inter-patch relationship reasoning, and fine-grained recognition without using object/part bounding box annotations during training.

    Comment: Accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021. Code available at https://github.com/google-research/google-research/tree/master/ptopk_patch_selection/
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 004 ; 006
    Publishing date 2021-04-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: Discovering relations between indirectly connected biomedical concepts.

    Weissenborn, Dirk / Schroeder, Michael / Tsatsaronis, George

    Journal of biomedical semantics

    2015  Volume 6, Page(s) 28

    Abstract: Background: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts ... ...

    Abstract Background: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation.
    Results: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely "has target", and "may treat", are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach.
    Conclusions: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.
    Language English
    Publishing date 2015
    Publishing country England
    Document type Journal Article
    ZDB-ID 2548651-2
    ISSN 2041-1480
    ISSN 2041-1480
    DOI 10.1186/s13326-015-0021-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book: Körperbehinderung und psychische Störung

    Reinhard, Hans-Georg / Weissenborn, Dirk-Michael Helmut

    1989  

    Author's details H. G. Reinhard u. M. Weißenborn
    Keywords Disabled Persons ; Mental Disorders ; Kind ; Körperbehinderung ; Psychische Verarbeitung
    Subject Verarbeitung ; Seelische Verarbeitung ; Körperliche Behinderung ; Kindheit ; Kindesalter ; Kindschaft ; Kinder
    Language German
    Size 125 S.
    Edition 1. Aufl.
    Publisher Verl. d. Acta Paedopsychiatrica
    Publishing place Düsseldorf
    Publishing country Germany
    Document type Book
    Note Literaturverz. S. 112 - 125
    HBZ-ID HT003333081
    ISBN 3-9802061-0-6 ; 978-3-9802061-0-5
    Database Catalogue ZB MED Medicine, Health

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  10. Article: Diagnostik der hepatischen Enzephalopathie. Diagnosis of hepatic encephalopathy

    Weissenborn, K. / Dirks, M. / Pflugrad, H.

    Verdauungskrankheiten

    2021  Volume 39, Issue 2, Page(s) 75

    Language German
    Document type Article
    ZDB-ID 605779-2
    ISSN 0174-738X
    Database Current Contents Medicine

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