Institution
National Research University – Higher School of Economics
Education•Moscow, Russia•
About: National Research University – Higher School of Economics is a education organization based out in Moscow, Russia. It is known for research contribution in the topics: Population & Politics. The organization has 12873 authors who have published 23376 publications receiving 256396 citations.
Papers published on a yearly basis
Papers
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TL;DR: VDJtools is reported, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API.
Abstract: Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple sclerosis patients we demonstrate that VDJtools greatly facilitates the analysis and leads to sound biological conclusions. VDJtools software and documentation are available at https://github.com/mikessh/vdjtools.
428 citations
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TL;DR: In this paper, the authors use the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015.
427 citations
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TL;DR: In this paper, the authors extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rate per weight.
Abstract: We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy.
424 citations
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TL;DR: This work developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of adjustability in generating molecular fingerprints; capacity of processing very large molecular data sets; and efficiency in unsupervised pretraining for regression model.
Abstract: Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.
420 citations
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University of Lausanne1, Heidelberg University2, Chinese Academy of Sciences3, National Research University – Higher School of Economics4, Skolkovo Institute of Science and Technology5, Cardiff University6, École Polytechnique Fédérale de Lausanne7, University of Kansas8, Children's Mercy Hospital9, University of Porto10, Francis Crick Institute11, The University of Texas Rio Grande Valley12, Linköping University13, German Primate Center14, Newcastle University15, CAS-MPG Partner Institute for Computational Biology16, Stanford University17
TL;DR: It is found that the breadth of gene expression and the extent of purifying selection gradually decrease during development, whereas the amount of positive selection and expression of new genes increase during development.
Abstract: The evolution of gene expression in mammalian organ development remains largely uncharacterized. Here we report the transcriptomes of seven organs (cerebrum, cerebellum, heart, kidney, liver, ovary and testis) across developmental time points from early organogenesis to adulthood for human, rhesus macaque, mouse, rat, rabbit, opossum and chicken. Comparisons of gene expression patterns identified correspondences of developmental stages across species, and differences in the timing of key events during the development of the gonads. We found that the breadth of gene expression and the extent of purifying selection gradually decrease during development, whereas the amount of positive selection and expression of new genes increase. We identified differences in the temporal trajectories of expression of individual genes across species, with brain tissues showing the smallest percentage of trajectory changes, and the liver and testis showing the largest. Our work provides a resource of developmental transcriptomes of seven organs across seven species, and comparative analyses that characterize the development and evolution of mammalian organs.
407 citations
Authors
Showing all 13307 results
Name | H-index | Papers | Citations |
---|---|---|---|
Rasmus Nielsen | 135 | 556 | 84898 |
Matthew Jones | 125 | 1161 | 96909 |
Fedor Ratnikov | 123 | 1104 | 67091 |
Kenneth J. Arrow | 113 | 411 | 111221 |
Wil M. P. van der Aalst | 108 | 725 | 42429 |
Peter Schmidt | 105 | 638 | 61822 |
Roel Aaij | 98 | 1071 | 44234 |
John W. Berry | 97 | 351 | 52470 |
Federico Alessio | 96 | 1054 | 42300 |
Denis Derkach | 96 | 1184 | 45772 |
Marco Adinolfi | 95 | 831 | 40777 |
Michael Alexander | 95 | 881 | 38749 |
Alexey Boldyrev | 94 | 439 | 32000 |
Shalom H. Schwartz | 94 | 220 | 67609 |
Richard Blundell | 93 | 487 | 61730 |