The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Zheng Xie - One of the best experts on this subject based on the ideXlab platform.
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predicting publication productivity for researchers a piecewise Poisson Model
Journal of Informetrics, 2020Co-Authors: Zheng XieAbstract:Abstract Predicting the publication productivity of research groups is a basic task for academic administrators and funding agencies. However, it is an elusive task due to diversity in researchers’ productivity patterns. This study proposed a Model for the dynamics of the productivity, inspired by the distribution feature of the number of a researcher's publications. It is a piecewise Poisson Model, analyzing and predicting the publication productivity of researchers by piecewise regression. The principle of the Model is built on the explanation for the distribution feature as a result of an inhomogeneous Poisson process that can be approximated as a piecewise Poisson process. The principle is validated by applying it on the high-quality dblp dataset. The effectiveness of the Model is tested on the dataset by fine fittings on the distribution of the number of publications for researchers, the evolutionary trend of their publication productivity, and the probability of producing publications. The Model has the advantage of providing results in an unbiased way; thus would be useful for funding agencies that evaluate a vast number of applications provided by research groups with a quantitative index on publications.
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predicting publication productivity for researchers a piecewise Poisson Model
arXiv: Digital Libraries, 2019Co-Authors: Zheng XieAbstract:Predicting the scientific productivity of researchers is a basic task for academic administrators and funding agencies. This study provided a Model for the publication dynamics of researchers, inspired by the distribution feature of researchers' publications in quantity. It is a piecewise Poisson Model, analyzing and predicting the publication productivity of researchers by regression. The principle of the Model is built on the explanation for the distribution feature as a result of an inhomogeneous Poisson process that can be approximated as a piecewise Poisson process. The Model's principle was validated by the high quality dblp dataset, and its effectiveness was testified in predicting the publication productivity for majority of researchers and the evolutionary trend of their publication productivity. Tests to confirm or disconfirm the Model are also proposed. The Model has the advantage of providing results in an unbiased way; thus is useful for funding agencies that evaluate a vast number of applications with a quantitative index on publications.
Liang Chen - One of the best experts on this subject based on the ideXlab platform.
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a two parameter generalized Poisson Model to improve the analysis of rna seq data
Nucleic Acids Research, 2010Co-Authors: Sudeep Srivastava, Liang ChenAbstract:Deep sequencing of RNAs (RNA-seq) has been a useful tool to characterize and quantify transcriptomes. However, there are significant challenges in the analysis of RNA-seq data, such as how to separate signals from sequencing bias and how to perform reasonable normalization. Here, we focus on a fundamental question in RNA-seq analysis: the distribution of the position-level read counts. Specifically, we propose a two-parameter generalized Poisson (GP) Model to the position-level read counts. We show that the GP Model fits the data much better than the traditional Poisson Model. Based on the GP Model, we can better estimate gene or exon expression, perform a more reasonable normalization across different samples, and improve the identification of differentially expressed genes and the identification of differentially spliced exons. The usefulness of the GP Model is demonstrated by applications to multiple RNA-seq data sets.
Jeanjacques Sotto - One of the best experts on this subject based on the ideXlab platform.
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graphical representation of a generalized linear Model based statistical test estimating the fit of the single hit Poisson Model to limiting dilution assays
Journal of Immunology, 2001Co-Authors: Thierry Bonnefoix, Philippe Bonnefoix, Mary Callanan, Paul Verdiel, Jeanjacques SottoAbstract:Standardized statistical and graphical methods for analysis of limiting dilution assays are highly desirable to enable investigators to compare and interpret results and conclusions with greater accuracy and precision. According to these requirements, we present in this work a powerful statistical slope test that estimates the fit of the single-hit Poisson Model to limiting dilution experiments. This method is readily amenable to a graphical representation. This slope test is obtained by Modeling limiting dilution data according to a linear log-log regression Model, which is a generalized linear Model specially designed for Modeling binary data. The result of the statistical slope test can then be graphed to visualize whether the data are compatible or not with the single-hit Poisson Model. We demonstrate this statistical test and its graphical representation by using two examples: a real limiting dilution experiment evaluating the growth frequency of IL-2-responsive tumor-infiltrating T cells in a malignant lymph node involved by a B cell non-Hodgkin’s lymphoma, and a simulation of a limiting dilution assay corresponding to a theoretical non-single-hit Poisson Model, suppressor two-target Poisson Model.
Sudeep Srivastava - One of the best experts on this subject based on the ideXlab platform.
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a two parameter generalized Poisson Model to improve the analysis of rna seq data
Nucleic Acids Research, 2010Co-Authors: Sudeep Srivastava, Liang ChenAbstract:Deep sequencing of RNAs (RNA-seq) has been a useful tool to characterize and quantify transcriptomes. However, there are significant challenges in the analysis of RNA-seq data, such as how to separate signals from sequencing bias and how to perform reasonable normalization. Here, we focus on a fundamental question in RNA-seq analysis: the distribution of the position-level read counts. Specifically, we propose a two-parameter generalized Poisson (GP) Model to the position-level read counts. We show that the GP Model fits the data much better than the traditional Poisson Model. Based on the GP Model, we can better estimate gene or exon expression, perform a more reasonable normalization across different samples, and improve the identification of differentially expressed genes and the identification of differentially spliced exons. The usefulness of the GP Model is demonstrated by applications to multiple RNA-seq data sets.
Thierry Bonnefoix - One of the best experts on this subject based on the ideXlab platform.
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Accurate hematopoietic stem cell frequency estimates by fitting multicell Poisson Models substituting to the single-hit Poisson Model in limiting dilution transplantation assays
Blood, 2010Co-Authors: Thierry Bonnefoix, Mary CallananAbstract:Limiting dilution transplantation assay (LDTA) is considered as the gold standard method to assess hematopoietic stem cell (HSC) content. Traditionally, HSC frequency estimates are based on the single-hit Poisson Model (SHPM), which posits that one donor HSC is sufficient to generate a progeny of detectable differentiated cells above a threshold value in hosts. However, there is no clear support for this statement, and it is receivable that more than one donor HSC may be necessary to provide detectable reconstitution in hosts above the threshold level for detection, usually 0.5% to 1% of donor-derived cells. To address this hypothesis, we evaluated the ability of a class of multiCell Poisson Models (C ≥1 PMs) to fit to LDTAs. In 7 of the 8 reanalyzed LDTAs, C ≥1 PMs plausibly compete with the traditional SHPM. Model averaging across the set of plausible Models gives 1.32- to 5.88-fold increases in HSC frequencies compared with the SHPM.
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graphical representation of a generalized linear Model based statistical test estimating the fit of the single hit Poisson Model to limiting dilution assays
Journal of Immunology, 2001Co-Authors: Thierry Bonnefoix, Philippe Bonnefoix, Mary Callanan, Paul Verdiel, Jeanjacques SottoAbstract:Standardized statistical and graphical methods for analysis of limiting dilution assays are highly desirable to enable investigators to compare and interpret results and conclusions with greater accuracy and precision. According to these requirements, we present in this work a powerful statistical slope test that estimates the fit of the single-hit Poisson Model to limiting dilution experiments. This method is readily amenable to a graphical representation. This slope test is obtained by Modeling limiting dilution data according to a linear log-log regression Model, which is a generalized linear Model specially designed for Modeling binary data. The result of the statistical slope test can then be graphed to visualize whether the data are compatible or not with the single-hit Poisson Model. We demonstrate this statistical test and its graphical representation by using two examples: a real limiting dilution experiment evaluating the growth frequency of IL-2-responsive tumor-infiltrating T cells in a malignant lymph node involved by a B cell non-Hodgkin’s lymphoma, and a simulation of a limiting dilution assay corresponding to a theoretical non-single-hit Poisson Model, suppressor two-target Poisson Model.
