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匹配条件: “Stoyan Georgiev” ,找到相关结果约438条。
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Randomized Dimension Reduction on Massive Data
Stoyan Georgiev,Sayan Mukherjee
Statistics , 2012,
Abstract: Scalability of statistical estimators is of increasing importance in modern applications and dimension reduction is often used to extract relevant information from data. A variety of popular dimension reduction approaches can be framed as symmetric generalized eigendecomposition problems. In this paper we outline how taking into account the low rank structure assumption implicit in these dimension reduction approaches provides both computational and statistical advantages. We adapt recent randomized low-rank approximation algorithms to provide efficient solutions to three dimension reduction methods: Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and Localized Sliced Inverse Regression (LSIR). A key observation in this paper is that randomization serves a dual role, improving both computational and statistical performance. This point is highlighted in our experiments on real and simulated data.
Eco-Biological Characteristics of Medicinal Plants in the Protected Area “Nahodishte Na Blatno Kokiche”, Gradina Village, Parvomay (Bulgaria)
Stoyan Georgiev,Alexander Tashev,Koycho Koev
Ecologia Balkanica , 2012,
Abstract: In the present work we investigated medicinal plants of the flora of the protected area “Nahodishte na blatno kokiche” the village of Gradina, Parvomay Municipality. Eco-biological characterization of the plants was done, and the species were grouped according to biological groups, life forms, floral elements and flowering time. Medicinal plants are also classified according to their attitude towards water, light and heat.
Adaptive Randomized Dimension Reduction on Massive Data
Gregory Darnell,Stoyan Georgiev,Sayan Mukherjee,Barbara E Engelhardt
Quantitative Biology , 2015,
Abstract: The scalability of statistical estimators is of increasing importance in modern applications. One approach to implementing scalable algorithms is to compress data into a low dimensional latent space using dimension reduction methods. In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages. We adapt recent randomized low-rank approximation algorithms to provide an efficient solution to principal component analysis (PCA), and we use this efficient solver to improve parameter estimation in large-scale linear mixed models (LMM) for association mapping in statistical and quantitative genomics. A key observation in this paper is that randomization serves a dual role, improving both computational and statistical performance by implicitly regularizing the covariance matrix estimate of the random effect in a LMM. These statistical and computational advantages are highlighted in our experiments on simulated data and large-scale genomic studies.
Evidence-ranked motif identification
Stoyan Georgiev, Alan P Boyle, Karthik Jayasurya, Xuan Ding, Sayan Mukherjee, Uwe Ohler
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-2-r19
Abstract: With the continuing growth and scale-up of genome and transcriptome sequencing of a large number of eukaryotes, there has been increasing interest in gaining a better understanding of the functional connections between all the genes within a complex organism. Regulatory factors that control the activation or repression of a gene on the transcriptional or post-transcriptional level often recognize specific DNA or RNA sequence elements. One of the first steps towards understanding the functional characteristics of regulators such as transcription factors (TFs) is to obtain accurate representations of their preferred binding sites and the location of their occurrences, which can then be utilized to identify candidate genes under direct regulatory influence of a TF. Regulatory elements tend to be short (about 6 to 15 bp in eukaryotes) and often highly degenerate, which makes it difficult to distinguish them from the surrounding sequence, which is orders of magnitude larger in size [1-3]. The task to identify a representation for a functional sequence element is commonly referred to as (de novo) motif finding.The motif finding problem has been traditionally phrased as the following: Given a set of putatively co-regulated genes, find the optimal motif description and the set of occurrence locations in the corresponding regulatory regions. Many popular approaches are based on iterative updating of a position-specific scoring matrix (PSSM) representation of the binding site, which reflects the affinity of the protein to its functional sites. Stochastic searches in the form of Gibbs sampling or expectation maximization-based algorithms have been used extensively to address this goal by means of iteratively optimizing a suitable objective function [4-7]. The use of additional information, such as sequences from related species (for example, [8]), or priors on the TF binding domain or nucleosome positions [9,10], has led to noticeable improvements in the performance of these s
PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data
David L Corcoran, Stoyan Georgiev, Neelanjan Mukherjee, Eva Gottwein, Rebecca L Skalsky, Jack D Keene, Uwe Ohler
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-8-r79
Abstract: RNA binding proteins (RBPs) play important roles in the life cycle of a transcript, from its nascence by RNA polymerase until its decay by RNases. All steps of RNA processing and function, including splicing, nuclear export, localization, stability, and small RNA-mediated regulation, are controlled by different RBPs and ribonucleoproteins [1]. The identification of which RBPs or ribonucleoproteins interact with which transcripts, how they interact, and where the interaction occurs, has been the focus of many studies. Recent advancements in high-throughput genomic technologies have resulted in profiles of transcriptome-wide RNA-protein interactions in vivo. Two of the most established methods for the investigation of these interactions are RIP-Chip [2] or RIP-seq [3,4] and crosslinking and immunoprecipitation (CLIP) [5]. RIP-Chip was the first method to use immunoprecipitation to identify RNA targets bound by specific RBPs at genome-wide scale [6]. Associated mRNAs are isolated, and then quantified using mRNA arrays or, more recently, subjected to high-throughput sequencing. This allows for the identification of all transcripts targeted by a particular RBP, but not for direct identification of where, or how many, RNA-protein interactions occur within a transcript. The second method, CLIP, typically uses short wave UV 254 nm crosslinking followed by immunoprecipitation and partial RNase digestion of the bound transcript. Conversion of the residual RNA segments into cDNA libraries and characterization by high-throughput sequencing yields small size windows in which the RNA-protein crosslinking occurred.PAR-CLIP (photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation) is a powerful modification of the CLIP technology for the isolation of protein-bound RNA segments [7]. Cells are first cultured with a photoreactive ribonucleoside analogue, typically 4-thiouridine (4SU), to boost RNA-protein crosslinking. This is followed by high-throughput sequenci
Intellectual Property Knowledge at the University’s Information Environment: A Comparative Study  [PDF]
Tereza Trencheva, Stoyan Denchev
Open Journal of Applied Sciences (OJAppS) , 2014, DOI: 10.4236/ojapps.2014.43013
Abstract: The paper aims to present some of the main findings from the first stage of realization of the scientific project of the State University of Library Studies and Information Technologies (SULSIT). “Analysis of the Common Practices in the Use of Products of Intellectual Property in University Information Environment” (2012-2014), financed by National Science Fund of the Bulgarian Ministry of Education and Science, Contract No. DMU 03/3-19.06.2012 in competition for “Young Scientists”, led by Dr. Teresa Trencheva. The project aims to explore the mind and culture of behavior among young people in Bulgaria, particularly students, to protect intellectual property on the Internet. The paper summarizes the results from the empirical study “Intellectual Property Protection on the Internet”, conducted among the students in nine Bulgarian universities accredited in the educational and professional field “Public Communication and Information Science.” The survey aims to explore, analyze and summarize the level of the respondents’ familiarity with the issues related to intellectual property protection from the lectures at the university, and what is also their attitude about the preservation and protection of copyright in the Internet. The data were accumulated in the period October-November 2012.
Global from the Start: The Characteristics of Born-Global Firms in the Technology Sector
Stoyan Tanev
Technology Innovation Management Review , 2012,
Abstract: This article provides insights from recent research on firms that are "born global". A born-global firm is a venture launched to exploit a global niche from the first day of its operations. The insights in this article are relevant to technology entrepreneurs and top management teams of new technology firms. After discussing various definitions for the term "born global" and identifying the main characteristics of born-global firms, this article lists a few salient characteristics of firms that are born global in the technology sector. The article concludes by identifying opportunities for future research.
Analyses of Methods and Algorithms for Modelling and Optimization of Biotechnological Processes
Stoyan Stoyanov
Bioautomation , 2009,
Abstract: A review of the problems in modeling, optimization and control of biotechnological processes and systems is given in this paper. An analysis of existing and some new practical optimization methods for searching global optimum based on various advanced strategies - heuristic, stochastic, genetic and combined are presented in the paper. Methods based on the sensitivity theory, stochastic and mix strategies for optimization with partial knowledge about kinetic, technical and economic parameters in optimization problems are discussed. Several approaches for the multi-criteria optimization tasks are analyzed. The problems concerning optimal controls of biotechnological systems are also discussed.
SIMULATION AND CHARACTERIZATION OF RANDOM SYSTEMS OF HARD PARTICLES
Dietrich Stoyan
Image Analysis and Stereology , 2002, DOI: 10.5566/ias.v21.ps41-s48
Abstract: This paper surveys methods for the simulation of random systems of hard particles, namely sedimentation and collective rearrangement algorithms, molecular dynamics, and Monte Carlo methods such as the Metropolis- Hastings algorithm. Furthermore, some set-theoretic statistical characteristics are discussed: the covariance and topological descriptors such as specific connectivity numbers and Meck.e's morphological functions.
Ventricular Beat Detection and Classification in Long Term ECG Recordings
Stoyan Tanev
International Journal Bioautomation , 2012,
Abstract: The QRS detection is key component of each automated ECG analysis. For this purpose a lot of QRS algorithms have been already developed. In the same time the number of new published methods continues to grow up. This implicitly proves the impossibility of building such detector that could totally cover the variety of all shapes of ventricular beats encountered in practice. Generally, limited studies on discrimination between normal (sinus) and ectopic beats are available. The paper describes very fast procedure for accurate QRS detection in long term ECG Holter recordings, followed by classification of the complexes in normal and ectopic. The algorithm was tested with the widely accepted AHA and MIT-BIH databases. The obtained sensitivity and specificity are comparable to other published results.
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