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  • 1
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    Bioconductor
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  • 2
    In: PLoS ONE, 2014, Vol.9(6)
    Description: Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Physical Sciences
    E-ISSN: 1932-6203
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  • 3
    Language: English
    In: Statistical applications in genetics and molecular biology, 2012, Vol.11(3), pp.Article 11
    Description: HIV patients are treated by administration of combinations of antiretroviral drugs. The very large number of such combinations makes the manual search for an effective therapy practically impossible, especially in advanced stages of the disease. Therapy selection can be supported by statistical methods...
    Keywords: Antiretroviral Therapy, Highly Active ; Models, Statistical ; HIV Infections -- Drug Therapy
    E-ISSN: 1544-6115
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  • 4
    In: Scientific Reports, 2015, Vol.5
    Description: Food - drug interactions are well studied, however much less is known about cuisine - drug interactions. Non-native cuisines are becoming increasingly more popular as they are available in (almost) all regions in the world. Here we address the problem of how known negative food - drug interactions are spread in different cuisines. We show that different drug categories have different distribution of the negative effects in different parts of the world. The effects certain ingredients have on different drug categories and in different cuisines are also analyzed. This analysis is aimed towards stressing out the importance of cuisine - drug interactions for patients which are being administered drugs with known negative food interactions. A patient being under a treatment with one such drug should be advised not only about the possible negative food - drug interactions, but also about the cuisines that could be avoided from the patient's diet.
    Keywords: Food ; Food ; Pharmacists;
    ISSN: 20452322
    E-ISSN: 20452322
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  • 5
    Language: English
    In: IEEE Transactions on Circuits and Systems I: Regular Papers, February 2014, Vol.61(2), pp.522-529
    Description: We study synchronization and consensus phenomena in state-dependent graphs in which the edges are weighted according to the Hebbian learning rule or its modified version. By exploring the master stability function of the synchronous state, we show that the modified Hebbian function as coupling strength enlarges the stability region of the synchronous state. In terms of consensus, given that the state-dependent weights are always positive, we prove that consensus in a network of multi-agent systems is always reachable. Furthermore, we show that in state-dependent graphs the second smallest eigenvalue of the graph Laplacian matrix has larger values due to the state-dependency, resulting in speed up of the convergence process.
    Keywords: Couplings ; Synchronization ; Oscillators ; Neurons ; Stability Analysis ; Eigenvalues and Eigenfunctions ; Laplace Equations ; Adaptive Coupling ; Consensus ; Hebbian Learning Rule ; Master-Stability Function ; State-Dependent Graphs ; Synchronization ; Engineering
    ISSN: 1549-8328
    E-ISSN: 1558-0806
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  • 6
    Language: English
    In: Bioinformatics (Oxford, England), 01 September 2010, Vol.26(17), pp.2085-92
    Description: As there exists no cure or vaccine for the infection with human immunodeficiency virus (HIV), the standard approach to treating HIV patients is to repeatedly administer different combinations of several antiretroviral drugs. Because of the large number of possible drug combinations, manually finding a successful regimen becomes practically impossible. This presents a major challenge for HIV treatment. The application of machine learning methods for predicting virological responses to potential therapies is a possible approach to solving this problem. However, due to evolving trends in treating HIV patients the available clinical datasets have a highly unbalanced representation, which might negatively affect the usefulness of derived statistical models. This article presents an approach that tackles the problem of predicting virological response to combination therapies by learning a separate logistic regression model for each therapy. The models are fitted by using not only the data from the target therapy but also the information from similar therapies. For this purpose, we introduce and evaluate two different measures of therapy similarity. The models are also able to incorporate phenotypic knowledge on the therapy outcomes through a Gaussian prior. With our approach we balance the uneven therapy representation in the datasets and produce higher quality models for therapies with very few training samples. According to the results from the computational experiments our therapy similarity model performs significantly better than training separate models for each therapy by using solely their examples. Furthermore, the model's performance is as good as an approach that encodes therapy information in the input feature space with the advantage of delivering better results for therapies with very few training samples. Code of the efficient logistic regression is available from http://www.mpi-inf.mpg.de/%7Ejasmina/fastLogistic.zip.
    Keywords: Artificial Intelligence ; Drug Therapy, Combination ; Logistic Models ; HIV Infections -- Drug Therapy
    ISSN: 13674803
    E-ISSN: 1367-4811
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  • 7
    Language: English
    In: Statistical applications in genetics and molecular biology, 2011, Vol.10, pp.Article 6
    Description: Infections with the human immunodeficiency virus type 1 (HIV-1) are treated with combinations of drugs. Unfortunately, HIV responds to the treatment by developing resistance mutations. Consequently, the genome of the viral target proteins is sequenced and inspected for resistance mutations as part of routine diagnostic procedures for ensuring an effective treatment. For predicting response to a combination therapy, currently available computer-based methods rely on the genotype of the virus and the composition of the regimen as input. However, no available tool takes full advantage of the knowledge about the order of and the response to previously prescribed regimens. The resulting high-dimensional feature space makes existing methods difficult to apply in a straightforward fashion. The machine learning system proposed in this work, sequence boosting, is tailored to exploiting such high-dimensional information, i.e. the extraction of longitudinal features, by utilizing the recent advancements in data mining and boosting. When applied to predicting the latest treatment outcome for 3,759 treatment-experienced patients from the EuResist integrated database, sequence boosting achieved superior performance compared to SVMs with RBF kernels. Moreover, sequence boosting allows an easy access to the discriminative treatment information. Analysis of feature importance values provided by our model confirmed known facts regarding HIV treatment. For instance, application of potent and recently licensed drugs was beneficial for patients, and, conversely, the patient group that was subject to NRTI mono-therapies in the past had poor treatment perspectives today. Furthermore, our model revealed novel biological insights. More precisely, the combination of previously used drugs with their in vivo response is more informative than the information of previously used drugs alone. Using this information improves the performance of systems for predicting therapy outcome.
    Keywords: Artificial Intelligence ; Anti-HIV Agents -- Therapeutic Use ; Data Mining -- Methods ; Drug Resistance, Viral -- Genetics ; HIV Infections -- Drug Therapy ; HIV-1 -- Genetics
    E-ISSN: 1544-6115
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  • 8
    In: AMIA Summits on Translational Science Proceedings, 2017, Vol.2017, p.239-248
    Description: We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods.
    Keywords: Articles
    E-ISSN: 2153-4063
    Source: U.S. National Library of Medicine (NIH/NLM)
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  • 9
    Language: English
    In: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017, Vol.2017, pp.239-248
    Description: We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods.
    ISSN: 2153-4063
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 10
    Language: English
    In: Parbhoo, Sonali, Jasmina Bogojeska, Maurizio Zazzi, Volker Roth, and Finale Doshi-Velez. 2017. “Combining Kernel and Model Based Learning for HIV Therapy Selection.” AMIA Summits on Translational Science Proceedings 2017 (1): 239-248.
    Description: We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods.
    Source: Harvard University Library
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