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  • 1.
    Hamon, Thierry
    et al.
    LIM&BIO (EA3969), Université Paris 13, France.
    Engström, Christopher
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Manser, Mounira
    Université Paris 13, France.
    Badji, Zina
    Universiteá Lille, Villeneuve d'Ascq, France.
    Grabar, Natalia
    Universiteá Lille, Villeneuve d'Ascq, France.
    Silvestrov, Sergei
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Combining Compositionality and Pagerank for the Identification of Semantic Relations between Biomedical Words2012In: BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing, 2012, p. 109-117Conference paper (Refereed)
    Abstract [en]

    The acquisition of semantic resources and relations is an important task for several applications, such as query expansion, information retrieval and extraction, machine translation. However, their validity should also be computed and indicated, especially for automatic systems and applications. We exploit the compositionality based methods for the acquisition of synonymy relations and of indicators of these synonyms. We then apply pagerank-derived algorithm to the obtained semantic graph in order to filter out the acquired synonyms. Evaluation performed with two independent experts indicates that the quality of synonyms is systematically improved by 10 to 15% after their filtering.

  • 2.
    Sanzogni, L.
    et al.
    Mälardalen University, Department of Mathematics and Physics. Griffith University, Australia.
    Chan, Ringo
    Griffith University, Australia.
    Bonner, Richard F.
    Mälardalen University, Department of Mathematics and Physics. Griffith University, Australia.
    Perceptrons with polynomial post-processing2000In: Journal of experimental and theoretical artificial intelligence (Print), ISSN 0952-813X, E-ISSN 1362-3079, Vol. 12, p. 57-68Article in journal (Refereed)
    Abstract [en]

    We introduce tensor product neural networks, composed of a layer of univariate neurons followed by a net of polynomial post-processing. We look at the general approximation properties of these networks observing in particular their relationship to the Stone-Weierstrass theorem for uniform function algebras. The implementation of the post-processing as a two-layer network, with logarithmic and exponential neurons leads to potentially important `generalized ’ product networks, which however require a complex approximation theory of Mu$ntz-Szasz-Ehrenpreis type. A back-propagation algorithm for product networks is presented and used in three computational experiments. In particular, approximation by a sigmoid product network is compared to that of a single layer radial basis network, and a multiple layer sigmoid network. An additional experiment is conducted, based on an operational system, to further demonstrate the versatility of the architecture.

  • 3.
    Weishaupt, Holger
    et al.
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
    Johansson, Patrik
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
    Engström, Christopher
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Nelander, Sven
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
    Silvestrov, Sergei
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Swartling, Fredrik
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden.
    Graph Centrality Based Prediction of Cancer Genes2016In: Engineering Mathematics II: Algebraic, Stochastic and Analysis Structures for Networks, Data Classification and Optimization / [ed] Sergei Silvestrov; Milica Rancic, Springer, 2016, p. 275-311Chapter in book (Refereed)
    Abstract [en]

    Current cancer therapies including surgery, radiotherapy and chemotherapy are often plagued by high failure rates. Designing more targeted and personalized treatment strategies requires a detailed understanding of druggable tumor drivergenes. As a consequence, the detection of cancer driver genes has evolved to a critical scientific field integrating both high-through put experimental screens as well as computational and statistical strategies. Among such approaches, network based prediction tools have recently been accentuated and received major focus due to their potential to model various aspects of the role of cancer genes in a biological system. In this chapter, we focus on how graph centralities obtained from biological networks have been used to predict cancer genes. Specifically, we start by discussing the current problems in cancer therapy and the reasoning behind using network based cancer gene prediction, followed by an outline of biological networks, their generation and properties. Finally, we review major concepts, recent results as well as future challenges regarding the use of graph centralities in cancer gene prediction.

  • 4.
    Weishaupt, Holger
    et al.
    Uppsala University, Sweden.
    Johansson, Patrik
    Uppsala University, Sweden.
    Engström, Christopher
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Nelander, Sven
    Uppsala University, Sweden.
    Silvestrov, Sergei
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Swartling, Fredrik
    Uppsala University, Sweden.
    Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks2017In: Methodology and Computing in Applied Probability, ISSN 1387-5841, E-ISSN 1573-7713, ISSN 1387-5841, Vol. 19, no 4, p. 1095-1105Article in journal (Refereed)
    Abstract [en]

    Graph centralities are commonly used to identify and prioritize disease genes in transcriptional regulatory networks. Studies on small networks of experimentally validated protein-protein interactions underpin the general validity of this approach and extensions of such findings have recently been proposed for networks inferred from gene expression data. However, it is largely unknown how well gene centralities are preserved between the underlying biological interactions and the networks inferred from gene expression data. Specifically, while previous studies have evaluated the performance of inference methods on synthetic gene expression, it has not been established how the choice of inference method affects individual centralities in the network. Here, we compare two gene centrality measures between reference networks and networks inferred from corresponding simulated gene expression data, using a number of commonly used network inference methods. The results indicate that the centrality of genes is only moderately conserved for all of the inference methods used. In conclusion, caution should be exercised when inspecting centralities in reverse-engineered networks and further work will be required to establish the use of such networks for prioritizing disease genes.

  • 5.
    Weishaupt, Holger
    et al.
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. Uppsala University, Sweden.
    Johansson, Patrik
    Engström, Christopher
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Nelander, Sven
    Silvestrov, Sergei
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Swartling, Fredrik J.
    Prediction of high centrality nodes from reverse-engineered transcriptional regulator networks2016In: Proocedings of the 4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop / [ed] Christos H Skiadas, 2016, p. 517-531Conference paper (Refereed)
    Abstract [en]

    The prioritization of genes based on their centrality in biological networkshas emerged as a promising technique for the prediction of phenotype related genes.A number of methods have been developed to derive one such type of network, i.e.transcriptional regulatory networks, from expression data. In order to reliably prioritizegenes from such networks, it is crucial to investigate how well the inferencemethods reconstruct the centralities that exist in the true biological system. We haverecently reported that the correlation of centrality rankings between reference andinferred networks is only modest when using an unbiased inference approach. In thisstudy we extend on these results and demonstrate that the correlation remains modestalso when using a biased inference utilizing a priori information about transcriptionfactors. However, we show further that despite this lack of a strong correlation, theinferred networks still allow a signicant prediction of genes with high centralities inthe reference networks.

  • 6.
    Weishaupt, Holger
    et al.
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University.
    Johansson, Patrik
    Engström, Christopher
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Nelander, Sven
    Silvestrov, Sergei
    Swartling, Fredrik J.
    Prioritization of candidate cancer genes on chromosome 17q through reverse engineered transcriptional regulatory networks in medulloblastoma groups 3 and 4Manuscript (preprint) (Other academic)
  • 7.
    Weishaupt, Holger
    et al.
    Uppsala University, Uppsala, Sweden.
    Johansson, Patrik
    Uppsala University, Uppsala, Sweden.
    Sundström, Anders
    Uppsala University, Uppsala, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, Skövde, Sweden.
    Olsson, Björn
    University of Skövde, Skövde, Sweden.
    Nelander, Sven
    Uppsala University, Uppsala, Sweden.
    Swartling, Fredrik J.
    Uppsala University, Uppsala, Sweden.
    Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genesIn: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811Article in journal (Refereed)
    Abstract [en]

    Motivation: Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential.

    Results: We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses.

  • 8.
    Weishaupt, Hrafn Holger
    Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics.
    Graph theory based approaches for gene prioritization in biological networks: Application to cancer gene detection in medulloblastoma2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Networks provide an intuitive and highly adaptable means to model relationships between objects. When translated to mathematical graphs, they become further amenable to a plethora of mathematical operations that allow a detailed study of the underlying relational data. Thus, it is not surprising that networks have evolved to a predominant method for analyzing such data in a vast variety of research fields. However, with increasing complexity of the studied problems, application of network modeling also becomes more challenging. Specifically, given a process to be studied, (i) which interactions are important and how can they be modeled, (ii) how can relationships be inferred from complex and potentially noisy data, and (iii) which methods should be used to test hypotheses or answer the relevant questions? This thesis explores the concept and challenges of network analysis in the context of a well-defined application area, i.e. the prediction of cancer genes from biological networks, with an application to medulloblastoma research.

    Medulloblastoma represents the most common malignant brain tumor in children. Currently about 70% of treated patients survive, but they often suffer from permanent cognitive sequelae. Medulloblastoma has previously been shown to harbor at least four distinct molecular subgroups. Related studies have also greatly advanced our understanding of the genetic aberrations associated with MB subgroups. However, to translate such findings to novel and improved therapy options, further insights are required into how the dysregulated genes interact with the rest of the cellular system, how such a cross-talk can drive tumor development, and how the arising tumorigenic processes can be targeted by drugs. Establishing such understanding requires investigations that can address biological processes at a more system-wide level, a task that can be approached through the study of cellular systems as mathematical networks of molecular interactions.

    This thesis discusses the identification of cancer genes from a network perspective, where specific focus is placed on one particular type of network, i.e. so called gene regulatory networks that model relationships between genes at the expression level. The thesis outlines the bridge between biological and mathematical network concepts. Specifically, the computational challenge of inferring such networks from molecular data is presented. Mathematical approaches for analyzing these networks are outlined and it is explored how such methods might be affected by network inference. Further focus is placed on dealing with the challenges of establishing a suitable gene expression dataset for network inference in MB. Finally, the thesis is concluded with an application of various network approaches in a hypothesis-driven study in MB, in which various novel candidate genes were prioritized.  

1 - 8 of 8
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