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arXiv 2018年

作者： Speicher, Nora K. Pfeifer, Nico Department of Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Methods in Medical Informatics Department of Computer Science University of Tübingen Germany

Due to the complexity of cancer, clustering algorithms have been used to disentangle the observed heterogeneity and identify cancer subtypes that can be treated specifically. While kernel based clustering approaches allow the use of more than one input matrix, which is an important factor when considering a multidimensional/manifold disease like cancer, the clustering results remain hard to evaluate and, in many cases, it is unclear which piece of information had which impact on the final result. In this paper, we propose an extension of multiple kernel learning clustering that enables the characterization of each identified patient cluster based on the features that had the highest impact on the result. To this end, we combine feature clustering with multiple kernel dimensionality reduction and introduce FIPPA, a score which measures the feature cluster impact on a patient cluster. Results: We applied the approach to different cancer types described by four different data types with the aim of identifying integrative patient subtypes and understanding which features were most important for their identification. Our results show that our method does not only have state-of-the-art performance according to standard measures (e.g., survival analysis), but, based on the high impact features, it also produces meaningful explanations for the molecular bases of the subtypes. This could provide an important step in the validation of potential cancer subtypes and enable the formulation of new hypotheses concerning individual patient groups. Similar analysis are possible for other disease phenotypes. Availability: Source code for rMKL-LPP with feature clustering and the calculation of fFIPPA scores is available at https://***/nora/fFIPPA. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

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All fingers are not the same: Handling variable-length sequences in a discriminative setting using conformal multi-instance kernels 17

All fingers are not the same: Handling variable-length seque...

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17th International Workshop on Algorithms in Bioinformatics, WABI 2017

作者： Nikumbh, Sarvesh Ebert, Peter Pfeifer, Nico Department of Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Department of Computer Science University of Tübingen Tübingen Germany

ISBN: (纸本)9783959770507

Most string kernels for comparison of genomic sequences are generally tied to using (absolute) positional information of the features in the individual sequences. This poses limitations when comparing variable-length sequences using such string kernels. For example, profiling chromatin interactions by 3C-based experiments results in variable-length genomic sequences (restriction fragments). Here, exact position-wise occurrence of signals in sequences may not be as important as in the scenario of analysis of the promoter sequences, that typically have a transcription start site as reference. Existing position-aware string kernels have been shown to be useful for the latter scenario. In this work, we propose a novel approach for sequence comparison that enables larger positional freedom than most of the existing approaches, can identify a possibly dispersed set of features in comparing variable-length sequences, and can handle both the aforementioned scenarios. Our approach, CoMIK, identifies not just the features useful towards classification but also their locations in the variable-length sequences, as evidenced by the results of three binary classification experiments, aided by recently introduced visualization techniques. Furthermore, we show that we are able to efficiently retrieve and interpret the weight vector for the complex setting of multiple multi-instance kernels. © Sarvesh Nikumbh, Peter Ebert, and Nico Pfeifer.

关键词： Classification (of information)

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Computing phylogenetic trees using topologically related minimum spanning trees

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Journal of Graph Algorithms and Applications 2017年第6期21卷 1003-1025页

作者： Kalaghatgi, Prabhav Lengauer, Thomas Department of Computational Biology and Applied Algorithmics Max-Planck Institut für Informatik Saarbrücken Germany

Choi et al. [2] introduced a minimum spanning tree (MST)-based method called CLGrouping, for constructing tree-structured probabilistic graphical models, a statistical framework that is commonly used for inferring phylogenetic trees. While CLGrouping works correctly if there is a unique MST, we observe an indeterminacy in the method in the case that there are multiple MSTs. We demonstrate the indeterminacy of CLGrouping using a synthetic quartet tree and a tree over primate genera. The indeterminacy of CLGrouping can be removed if the input MST shares a topological relationship with the corresponding phylogenetic tree. We introduce so-called vertex order based MSTs (VMSTs) that are guaranteed to have the desired topological relationship. We relate the number of leaves in the VMST to the degree of parallelism that is offered by CLGrouping. We provide polynomial-time algorithms for constructing VMSTs and for selecting a VMST with the optimal number of leaves. © 2017, Brown University. All rights reserved.

关键词： Polynomial approximation

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Statistical Learning in computational biology

Statistical Learning in Computational Biology

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2015 German Conference on Bioinformatics, GCB 2015

作者： Pfeifer, Nico Department of Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Germany

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From Predicting to Analyzing HIV-1 Resistance to Broadly Neutralizing Antibodies

From Predicting to Analyzing HIV-1 Resistance to Broadly Neu...

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2015 German Conference on Bioinformatics, GCB 2015

作者： Feldmann, Anna Pfeifer, Nico Computational Biology and Applied Algorithmics Department Max Planck Institute for Informatics Saarbrücken Germany Saarbrücken Graduate School of Computer Science Saarland University Saarbrücken Germany

Treatment with broadly neutralizing antibodies (bNAbs) has recently proven effective against HIV-1 infections in humanized mice, non-human primates, and humans. For optimal treatment, susceptibility of the patient's viral strains to a particular bNAb has to be ensured. Since no computational approaches are so far available, susceptibility can only be tested in expensive and time-consuming neutralization experiments. Here, we present well-performing computational models (AUC up to 0.84) that can predict HIV-1 resistance to bNAbs given the envelope sequence of the virus. Having learnt important binding sites of the bNAbs from the envelope sequence, the models are also biologically meaningful and useful for epitope recognition. Additional to the prediction result, we provide a motif logo that displays the contribution of the pivotal residues of the test sequence to the prediction. As our prediction models are based on non-linear kernels, we introduce a new visualization technique to improve the model interpretability. Moreover, we confirmed previous experimental findings that there is a trend towards antibody resistance for the subtype B population of the virus. While previous experiments considered rather small and selected cohorts, we were able to show a similar trend for the global HIV-1 population comprising all major subtypes by predicting the neutralization sensitivity for around 36,000 HIV-1 sequences - a scale-up which is very difficult to achieve in an experimental setting. © 2015 German Conference on Bioinformatics, GCB 2015. All rights reserved.

关键词： Viruses

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Personalized HIV therapy to control drug resistance

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Drug Discovery Today: Technologies 2014年第1期11卷 57-64页

作者： Lengauer, Thomas Pfeifer, Nico Kaiser, Rolf Department of Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Saarbrücken Germany Institute of Virology University of Cologne Germany

The therapy of HIV patients is characterized by both the high genomic diversity of the virus population harbored by the patient and a substantial volume of therapy options. The virus population is unique for each patient and time point. The large number of therapy options makes it difficult to select an optimal or near optimal therapy, especially with therapy-experienced patients. In the past decade, computer-based support for therapy selection, which assesses the level of viral resistance against drugs has become a mainstay for HIV patients. We discuss the properties of available systems and the perspectives of the field. © 2014 The Authors.

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Interpretable per case weighted ensemble method for cancer associations

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28th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy, DBSEC 2014

作者： Jalali, Adrin Pfeifer, Nico Department of Computational Biology and Applied Algorithmics Max Planck Institute for Informatics Campus E1 4 Saarbrücken66123 Germany Saarbrücken Graduate School of Computer Science Saarland University Saarbrücken Germany

ISBN: (纸本)9783662447529

Over the past decades, biology has transformed into a high throughput research field both in terms of the number of different measurement techniques as well as the amount of variables measured by each technique (e.g., from Sanger sequencing to deep sequencing) and is more and more targeted to individual cells [3]. This has led to an unprecedented growth of biological information. Consequently, techniques that can help researchers find the important insights of the data are becoming more and more important. Molecular measurements from cancer patients such as gene expression and DNA methylation are usually very noisy. Furthermore, cancer types can be very heterogeneous. Therefore, one of the main assumptions for machine learning, that the underlying unknown distribution is the same for all samples in training and test data, might not be completely fulfilled. In this work, we introduce a method that is aware of this potential bias and utilizes an estimate of the differences during the generation of the final prediction method. For this, we introduce a set of sparse classifiers based on L1-SVMs [1], under the constraint of disjoint features used by classifiers. Furthermore, for each feature chosen by one of the classifiers, we introduce a regression model based on Gaussian process regression that uses additional features. For a given test sample we can then use these regression models to estimate for each classifier how well its features are predictable by the corresponding Gaussian process regression model. This information is then used for a confidence-based weighting of the classifiers for the test sample. Schapire and Singer showed that incorporating confidences of classifiers can improve the performance of an ensemble method [2]. However, in their setting confidences of classifiers are estimated using the training data and are thus fixed for all test samples, whereas in our setting we estimate confidences of individual classifiers per given test sample. In our eval

关键词： Gene expression

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Low k-shells identify bridge elements critical to disease flow in small-world networks

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AIP Conference Proceedings 2012年第1期1479卷 1426-1429页

作者： A. I. Reppas G. Lawyer Department of Computational Biology and Applied Algorithmics Max-Planck-Institut für Informatik Campus E1 4 66123 Saarbrücken Germany

Targeted vaccination of individuals with high degree or centrality has been shown to be an effective strategy in scale-free networks. Small-world networks, however, are characterized by homogenous degree and centrality distributions, making it less obvious which individuals should be targeted. Under the assumption that nodes with low k-shell index serve as bridge elements, we confirm via simulation that an acquaintance-based vaccination strategy based on low k-shell individuals can efficiently control disease in small world networks.

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O116. The EuResist expert model for customised HAART optimisation: 2010 update and extension to newest compounds

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Journal of the International AIDS Society 2010年第4期13卷 1-2页

作者： A Pironti A Sönnerborg M Zazzi R Kaiser D Struck B Clotet ÀM Vandamme F Incardona T Lengauer M Rosen-Zvi M Prosperi Max-Planck-Institut für Informatik Computational Biology and Applied Algorithmics Saarbrücken Germany Karolinska Institutet Clinical Virology/Infectious Diseases Stockholm Sweden Università degli Studi di Siena Molecular Biology Department Siena Italy Universitätsklinikum Köln Institut für Virologie Cologne Germany Centre de Recherche Public de la Santé Laboratory of Retrovirology Luxembourg Luxembourg IrsiCaixa Barcelona Spain Katholieke Universiteit Leuven Clinical and Epidemiological Virology Leuven Belgium Informa S.r.l. Research and Design Rome Italy BM Haifa Research Labs Machine Learning and Data Mining Haifa Israel Catholic University of the Sacred Heart Clinic of Infectious Diseases Rome Italy

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Erratum to: Prevalence and characteristics of hepatitis B and C virus infections in treatment-naïve HIV-infected patients

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Medical Microbiology and Immunology 2010年第1期200卷 51-51页

作者： Stefan Reuter Mark Oette Frank Clemens Wilhelm Bastian Beggel Rolf Kaiser Melanie Balduin Finja Schweitzer Jens Verheyen Ortwin Adams Thomas Lengauer Gerd Fätkenheuer Herbert Pfister Dieter Häussinger Clinic of Gastroenterology Hepatology and Infectious Diseases University Hospital Duesseldorf Germany Clinic of General Medicine Gastroenterology and Infectious Diseases Augustinerinnen Hospital Cologne Germany Institute of Virology University of Cologne Cologne Germany Max Planck Institute for Informatics Computational Biology and Applied Algorithmics Saarbruecken Germany Institute of Virology University Hospital of Düsseldorf Düsseldorf Germany Department of Internal Medicine I University Hospital of Cologne Cologne Germany

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